A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization

Size: px
Start display at page:

Download "A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization"

Transcription

1 A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization Dr. Liu Dasheng James Cook University, Singapore / 48

2 Outline of Talk. Particle Swam Optimization 2. Multiobjective Particle Swarm Optimization 3. A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization 4. Research Ideas 2 / 48

3 Particle Swarm Optimization Particle swarm optimization (PSO) was first introduced by James Kennedy (a social psychologist) and Russell Eberhart (an electrical engineer) in 995, which originates from the simulation of behavior of bird flocks. 3 / 48

4 Particle Swarm Optimization There are a number of algorithms to simulate the movement of a bird flock or fish school. Kennedy and Eberhart became particularly interested in the models developed by Heppner (a zoologist) [62]. 4 / 48

5 Heppner s Model In Heppner s model, birds would begin by flying around with no particular destination and in spontaneously formed flocks until one of the birds flew over the roosting area. 5 / 48

6 Particle Swarm Optimization To Eberhart and Kennedy, finding a roost is analogous to finding a good solution in the field of possible solutions. They revised Heppner s methodology so that particles will fly over a solution space and try to find the best solution depending on their own discoveries and past experiences of their neighbors. 6 / 48

7 Working Principle of PSO 7 / 48

8 Original Version In the original version of PSO, each individual is treated as a volume-less particle in the D dimensional solution space. The equations for calculating velocity and position of particles are shown below: 8 / 48

9 Adjustable Step Size Further research shows that to adjust velocity not by a fixed step size but according to the distance between current position and best position can improve performance. 9 / 48

10 V max One parameter V max is introduced, and the particle s velocity on each dimension cannot exceed V max. If V max is too large, particle may fly past good solutions. If V max is too small, particle may not explore sufficiently beyond locally good regions. V max is usually set at -2% of the dynamic range of each dimension. / 48

11 Inertial Weight To better control the exploration and exploitation in particle swarm optimization, the concept of inertial weight (w) was developed. v w v c r ( p x ) k k k k k i, d i, d i, d i, d c r ( p x ) k k k 2 2 g, d i, d / 48

12 Particle Swarm Optimization Formula Each individual in PSO is assigned a random velocity and flies across the solution space with a memory of its own best position called pbest and a knowledge of the whole swarm s global best position called gbest. v w v c r ( p x ) k k k k k i, d i, d i, d i, d c r ( p x ) x x v k k k 2 2 g, d i, d k k k i, d i, d i, d w is the inertia weight; c is the cognition weight and c 2 is the social weight; r and r 2 are two random values uniformly distributed in the range of [, ]. 2 / 48

13 Terminology 3 / 48

14 Terminology 4 / 48

15 Multiobjective Optimization Real world problems usually involve simultaneous optimization of several competing objectives. Solution exists in the form of alternative tradeoffs. Non-inferior solutions are known as nondominated solutions The set of nondominated solutions form the Pareto solution set A minimization problem Trade-off Curve f 2 Unfeasible Region f 5 / 48

16 MOPSO Conventional optimization search techniques Hardly handle multiple objectives The gradients need to be well-defined and differentiable May trap in local optima Multi-objective particle swarm optimization (MOPSO) is a powerful tool for solving MO optimization problems. Capable of searching for the global trade-off. Maintain a diverse set of solutions Robust and applicable to a wide variety of problems. f 2 f 6 / 48

17 MOPSO 7 / 48

18 Minimization Performance Assessments For MOO, performance metrics must be able to measure quality in terms of: Diversity. Proximity between the generated and true Pareto f 2 Non-dominated solution Pareto Frontier Non-dominated set front. Minimization f 8 / 48

19 Performance Measures Generational Distance (GD) (Veldhuizen, 999) Represents how far the evolved solution set is from the true Pareto front. Spacing (S) (Schott, 995) Measures how evenly evolved solutions distribute itself. Maximum Spread (MS) (Zitzler, 999) Measures how well the true Pareto front is covered by the evolved solution set. 9 / 48

20 Problem Test Suite ZDT ZDT2 Test Problem Features ZDT Pareto front is convex. 2 ZDT2 Pareto front is non-convex. 3 ZDT3 Pareto front consists of several noncontiguous convex parts. 4 ZDT4 Pareto front is highly multi-modal where there are 2 9 local Pareto fronts. 5 ZDT6 The Pareto optimal solutions are non-uniformly distributed along the global Pareto front. The density of the solutions is low near the Pareto front and high away from the front ZDT3 ZDT4 ZDT6 2 / 48

21 Problem Test Suite Test Problem Features 6 FON Pareto front is non-convex. 7 KUR Pareto front consists of several noncontiguous convex parts. 8 POL Pareto front and Pareto optimal solutions consist of several noncontiguous convex parts FON KUR POL 2 / 48

22 Two modification introduced to improve performance Fuzzy global best Synchronous particle local search Memetic algorithm : evolution algorithm with local improvement technique 22 / 48

23 Two Modifications Fuzzy Global Best (f-gbest) A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. Synchronous Particle Local Search (SPLS) Hybridized with a directed local search operator for local fine tuning, which helps to discover a well-distributed Pareto front. 23 / 48

24 Fuzzy gbest Fuzzy Global Best (f-gbest) accounts for the uncertainty of global best knowledge to prevent premature convergence Incorporates fuzzy number x to represent global best. F-gbest is characterized by normal distribution. Degree of uncertainty reduces with generations synonymous with information gain. Search trajectory using conventional gbest gbest Possible location of gbest Search Region incorporating f-gbest Particle x2 x3 24 / 48

25 Formula for Fuzzy gbest The calculation of particle velocity can be rewritten as k k p N( p, ) c, d g, d f( k) k k k k k k k k v w v c r ( p x ) c r ( p x ) i, d i, d i, d i, d 2 2 c, d i, d f-gbest is characterized by a normal distribution, N( p k, ), where g, d representing the degree of uncertainty about the optimality of the global-best. 25 / 48

26 Synchronous Particle Local Search SPLS of assimilated particles along x and x3 x Assimilated Particle A' Trajectory along assigned search direction, x A B Possible location of assigned gbest Trajectory along assigned search direction, x3 Assimilated Particle B' x2 x3 26 / 48

27 Synchronous Particle Local Search SPLS is performed in the vicinity of the particles. SPLS: Select S LS particles randomly from particle swarm Select N LS nondominated particles from the archive with the best niche count into a selection pool Assign an arbitrary nondominated solution from the selection pool to each of the S particles as gbest Assign an arbitrary dimension to each of the LS SLS particles Assimilation: With the exception of the assigned dimension, update the position of S LS particles in the desion space with the selected gbest position Update the position of all SLS assimilated particles using fuzzy gbest along the pre-assigned dimension 27 / 48

28 Implementation Initialize Particle Swarm Evaluate Particles Return Archive Archiving Update Particle Position using f-gbest SPLS Yes cycle = max_cycles? Select S LS particles for SPLS No Select Personal Best Select Global Best 28 / 48

29 Simulation Results (a) (b) (c) (d) (e) (f) Evolved tradeoffs by a) FMOPSO, b) CMOPSO, c) SMOPSO, d) IMOEA, e) NSGA II, and f) SPEA2 for ZDT 29 / 48

30 Simulation Results (a) (b) (c) (d) (e) (f) (g) (i) (h) Statistical performance of the different algorithms: a) GD, b) MS, c) S for ZDT4; d) GD, e) MS, f) S for ZDT6; and g) GD, i) MS, h) S for FON 3 / 48

31 Research Ideas Researchers are facing the challenge of increasing dimensionality and computational cost of today s applications. Handling of high dimensional problems. Use of dimensional reduction techniques. Use of learning techniques to gain information on the shape and position of Pareto front/pareto set. Apply surrogates to reduce evaluation time. Surrogate models are cheap and approximate evaluation models. Solving real world problems of your interest 3 / 48

32 The Papers Zhinzhong Ding, Fuqiang Lu, and Hualing Bi, A TWO-STAGE PARTICLE SWARM OPTIMIZATION FOR VIRTUAL ENTERPRISE RISK MANAGEMENT, International Journal of Innovative Computing, Information and Control, vol., no. 4, pp , 24. Marco Corazza, Giovanni Fasano, S. Y., and Riccardo Gusso, Particle Swarm Optimization with non-smooth penalty reformulation, for a complex portfolio selection problem, Applied Mathematics and Computation 244, pp , 23. Kuo, R. J. and Hong, C. K. Integration of Genetic Algorithm and Particle Swarm Optimization for Investment Portfolio Optimization, Applied Mathematics & Information Sciences, vol. 7, no. 6, pp , 23. Jui-Fang Chang, Peng Shi, Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio, Information Sciences 8, pp , 2 Liu, D. S., Tan, K. C., Goh, C. K. and Ho, W. K., A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization, IEEE Transactions on Systems, Man and Cybernetics: Part B (Cybernetics), vol. 37, no., pp. 42-5, / 48

A *69>H>N6 #DJGC6A DG C<>C::G>C<,8>:C8:H /DA 'D 2:6G, ()-"&"3 -"(' ( +-" " " % '.+ % ' -0(+$,

A *69>H>N6 #DJGC6A DG C<>C::G>C<,8>:C8:H /DA 'D 2:6G, ()-&3 -(' ( +-   % '.+ % ' -0(+$, The structure is a very important aspect in neural network design, it is not only impossible to determine an optimal structure for a given problem, it is even impossible to prove that a given structure

More information

Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization

Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization Handling Multi Objectives of with Multi Objective Dynamic Particle Swarm Optimization Richa Agnihotri #1, Dr. Shikha Agrawal #1, Dr. Rajeev Pandey #1 # Department of Computer Science Engineering, UIT,

More information

MLPSO: MULTI-LEADER PARTICLE SWARM OPTIMIZATION FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

MLPSO: MULTI-LEADER PARTICLE SWARM OPTIMIZATION FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS MLPSO: MULTI-LEADER PARTICLE SWARM OPTIMIZATION FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Zuwairie Ibrahim 1, Kian Sheng Lim 2, Salinda Buyamin 2, Siti Nurzulaikha Satiman 1, Mohd Helmi Suib 1, Badaruddin

More information

Adaptive Multi-objective Particle Swarm Optimization Algorithm

Adaptive Multi-objective Particle Swarm Optimization Algorithm Adaptive Multi-objective Particle Swarm Optimization Algorithm P. K. Tripathi, Sanghamitra Bandyopadhyay, Senior Member, IEEE and S. K. Pal, Fellow, IEEE Abstract In this article we describe a novel Particle

More information

Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization

Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization M. Shahab Alam, M. Usman Rafique, and M. Umer Khan Abstract Motion planning is a key element of robotics since it empowers

More information

Particle Swarm Optimization

Particle Swarm Optimization Dario Schor, M.Sc., EIT schor@ieee.org Space Systems Department Magellan Aerospace Winnipeg Winnipeg, Manitoba 1 of 34 Optimization Techniques Motivation Optimization: Where, min x F(x), subject to g(x)

More information

EVOLUTIONARY algorithms (EAs) are a class of

EVOLUTIONARY algorithms (EAs) are a class of An Investigation on Evolutionary Gradient Search for Multi-objective Optimization C. K. Goh, Y. S. Ong and K. C. Tan Abstract Evolutionary gradient search is a hybrid algorithm that exploits the complementary

More information

Traffic Signal Control Based On Fuzzy Artificial Neural Networks With Particle Swarm Optimization

Traffic Signal Control Based On Fuzzy Artificial Neural Networks With Particle Swarm Optimization Traffic Signal Control Based On Fuzzy Artificial Neural Networks With Particle Swarm Optimization J.Venkatesh 1, B.Chiranjeevulu 2 1 PG Student, Dept. of ECE, Viswanadha Institute of Technology And Management,

More information

Particle Swarm Optimization Approach for Scheduling of Flexible Job Shops

Particle Swarm Optimization Approach for Scheduling of Flexible Job Shops Particle Swarm Optimization Approach for Scheduling of Flexible Job Shops 1 Srinivas P. S., 2 Ramachandra Raju V., 3 C.S.P Rao. 1 Associate Professor, V. R. Sdhartha Engineering College, Vijayawada 2 Professor,

More information

Part II. Computational Intelligence Algorithms

Part II. Computational Intelligence Algorithms Part II Computational Intelligence Algorithms 126 Chapter 5 Population-based Single-objective Algorithms One bee makes no swarm. French proverb This chapter provides an overview of two CI algorithms that

More information

Modified Particle Swarm Optimization

Modified Particle Swarm Optimization Modified Particle Swarm Optimization Swati Agrawal 1, R.P. Shimpi 2 1 Aerospace Engineering Department, IIT Bombay, Mumbai, India, swati.agrawal@iitb.ac.in 2 Aerospace Engineering Department, IIT Bombay,

More information

PARTICLE SWARM OPTIMIZATION (PSO)

PARTICLE SWARM OPTIMIZATION (PSO) PARTICLE SWARM OPTIMIZATION (PSO) J. Kennedy and R. Eberhart, Particle Swarm Optimization. Proceedings of the Fourth IEEE Int. Conference on Neural Networks, 1995. A population based optimization technique

More information

CHAPTER 6 ORTHOGONAL PARTICLE SWARM OPTIMIZATION

CHAPTER 6 ORTHOGONAL PARTICLE SWARM OPTIMIZATION 131 CHAPTER 6 ORTHOGONAL PARTICLE SWARM OPTIMIZATION 6.1 INTRODUCTION The Orthogonal arrays are helpful in guiding the heuristic algorithms to obtain a good solution when applied to NP-hard problems. This

More information

A MULTI-SWARM PARTICLE SWARM OPTIMIZATION WITH LOCAL SEARCH ON MULTI-ROBOT SEARCH SYSTEM

A MULTI-SWARM PARTICLE SWARM OPTIMIZATION WITH LOCAL SEARCH ON MULTI-ROBOT SEARCH SYSTEM A MULTI-SWARM PARTICLE SWARM OPTIMIZATION WITH LOCAL SEARCH ON MULTI-ROBOT SEARCH SYSTEM BAHAREH NAKISA, MOHAMMAD NAIM RASTGOO, MOHAMMAD FAIDZUL NASRUDIN, MOHD ZAKREE AHMAD NAZRI Department of Computer

More information

A NEW APPROACH TO SOLVE ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION

A NEW APPROACH TO SOLVE ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION A NEW APPROACH TO SOLVE ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION Manjeet Singh 1, Divesh Thareja 2 1 Department of Electrical and Electronics Engineering, Assistant Professor, HCTM Technical

More information

Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization

Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization Experimental Study on Bound Handling Techniques for Multi-Objective Particle Swarm Optimization adfa, p. 1, 2011. Springer-Verlag Berlin Heidelberg 2011 Devang Agarwal and Deepak Sharma Department of Mechanical

More information

LECTURE 16: SWARM INTELLIGENCE 2 / PARTICLE SWARM OPTIMIZATION 2

LECTURE 16: SWARM INTELLIGENCE 2 / PARTICLE SWARM OPTIMIZATION 2 15-382 COLLECTIVE INTELLIGENCE - S18 LECTURE 16: SWARM INTELLIGENCE 2 / PARTICLE SWARM OPTIMIZATION 2 INSTRUCTOR: GIANNI A. DI CARO BACKGROUND: REYNOLDS BOIDS Reynolds created a model of coordinated animal

More information

International Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 7, February 2013)

International Journal of Digital Application & Contemporary research Website:   (Volume 1, Issue 7, February 2013) Performance Analysis of GA and PSO over Economic Load Dispatch Problem Sakshi Rajpoot sakshirajpoot1988@gmail.com Dr. Sandeep Bhongade sandeepbhongade@rediffmail.com Abstract Economic Load dispatch problem

More information

Improvement research of genetic algorithm and particle swarm optimization algorithm based on analytical mathematics

Improvement research of genetic algorithm and particle swarm optimization algorithm based on analytical mathematics Acta Technica 62 No. 1B/2017, 551 560 c 2017 Institute of Thermomechanics CAS, v.v.i. Improvement research of genetic algorithm and particle swarm optimization algorithm based on analytical mathematics

More information

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM Journal of Al-Nahrain University Vol.10(2), December, 2007, pp.172-177 Science GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM * Azhar W. Hammad, ** Dr. Ban N. Thannoon Al-Nahrain

More information

Particle Swarm Optimization

Particle Swarm Optimization Particle Swarm Optimization Gonçalo Pereira INESC-ID and Instituto Superior Técnico Porto Salvo, Portugal gpereira@gaips.inesc-id.pt April 15, 2011 1 What is it? Particle Swarm Optimization is an algorithm

More information

Reconfiguration Optimization for Loss Reduction in Distribution Networks using Hybrid PSO algorithm and Fuzzy logic

Reconfiguration Optimization for Loss Reduction in Distribution Networks using Hybrid PSO algorithm and Fuzzy logic Bulletin of Environment, Pharmacology and Life Sciences Bull. Env. Pharmacol. Life Sci., Vol 4 [9] August 2015: 115-120 2015 Academy for Environment and Life Sciences, India Online ISSN 2277-1808 Journal

More information

Designing of Optimized Combinational Circuits Using Particle Swarm Optimization Algorithm

Designing of Optimized Combinational Circuits Using Particle Swarm Optimization Algorithm Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 8 (2017) pp. 2395-2410 Research India Publications http://www.ripublication.com Designing of Optimized Combinational Circuits

More information

Feature weighting using particle swarm optimization for learning vector quantization classifier

Feature weighting using particle swarm optimization for learning vector quantization classifier Journal of Physics: Conference Series PAPER OPEN ACCESS Feature weighting using particle swarm optimization for learning vector quantization classifier To cite this article: A Dongoran et al 2018 J. Phys.:

More information

Week 9 Computational Intelligence: Particle Swarm Optimization

Week 9 Computational Intelligence: Particle Swarm Optimization Week 9 Computational Intelligence: Particle Swarm Optimization Mudrik Alaydrus Faculty of Computer Sciences University of Mercu Buana, Jakarta mudrikalaydrus@yahoo.com Presentasi Mudrik Alaydrus 8Mudrik

More information

CHAPTER 2 MULTI-OBJECTIVE REACTIVE POWER OPTIMIZATION

CHAPTER 2 MULTI-OBJECTIVE REACTIVE POWER OPTIMIZATION 19 CHAPTER 2 MULTI-OBJECTIE REACTIE POWER OPTIMIZATION 2.1 INTRODUCTION In this chapter, a fundamental knowledge of the Multi-Objective Optimization (MOO) problem and the methods to solve are presented.

More information

Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients Information Sciences 77 (27) 533 549 www.elsevier.com/locate/ins Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients Praveen Kumar Tripathi *, Sanghamitra

More information

An improved PID neural network controller for long time delay systems using particle swarm optimization algorithm

An improved PID neural network controller for long time delay systems using particle swarm optimization algorithm An improved PID neural network controller for long time delay systems using particle swarm optimization algorithm A. Lari, A. Khosravi and A. Alfi Faculty of Electrical and Computer Engineering, Noushirvani

More information

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India.

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India. Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Training Artificial

More information

A novel Ranking-based Optimal Guides Selection Strategy in MOPSO

A novel Ranking-based Optimal Guides Selection Strategy in MOPSO Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 9 ( ) Information Technology and Quantitative Management (ITQM ) A novel Ranking-based Optimal Guides Selection Strategy

More information

Application of Multiobjective Particle Swarm Optimization to maximize Coverage and Lifetime of wireless Sensor Network

Application of Multiobjective Particle Swarm Optimization to maximize Coverage and Lifetime of wireless Sensor Network Application of Multiobjective Particle Swarm Optimization to maximize Coverage and Lifetime of wireless Sensor Network 1 Deepak Kumar Chaudhary, 1 Professor Rajeshwar Lal Dua 1 M.Tech Scholar, 2 Professors,

More information

An Evolutionary Multi-Objective Crowding Algorithm (EMOCA): Benchmark Test Function Results

An Evolutionary Multi-Objective Crowding Algorithm (EMOCA): Benchmark Test Function Results Syracuse University SURFACE Electrical Engineering and Computer Science College of Engineering and Computer Science -0-005 An Evolutionary Multi-Objective Crowding Algorithm (EMOCA): Benchmark Test Function

More information

IMPROVING THE PARTICLE SWARM OPTIMIZATION ALGORITHM USING THE SIMPLEX METHOD AT LATE STAGE

IMPROVING THE PARTICLE SWARM OPTIMIZATION ALGORITHM USING THE SIMPLEX METHOD AT LATE STAGE IMPROVING THE PARTICLE SWARM OPTIMIZATION ALGORITHM USING THE SIMPLEX METHOD AT LATE STAGE Fang Wang, and Yuhui Qiu Intelligent Software and Software Engineering Laboratory, Southwest-China Normal University,

More information

A local multiobjective optimization algorithm using neighborhood field

A local multiobjective optimization algorithm using neighborhood field Struct Multidisc Optim (22) 46:853 87 DOI.7/s58-2-8-x RESEARCH PAPER A local multiobjective optimization algorithm using neighborhood field Zhou Wu Tommy W. S. Chow Received: 22 September 2 / Revised:

More information

Inertia Weight. v i = ωv i +φ 1 R(0,1)(p i x i )+φ 2 R(0,1)(p g x i ) The new velocity update equation:

Inertia Weight. v i = ωv i +φ 1 R(0,1)(p i x i )+φ 2 R(0,1)(p g x i ) The new velocity update equation: Convergence of PSO The velocity update equation: v i = v i +φ 1 R(0,1)(p i x i )+φ 2 R(0,1)(p g x i ) for some values of φ 1 and φ 2 the velocity grows without bound can bound velocity to range [ V max,v

More information

Proceedings of the 5th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 16-18, 2006 (pp )

Proceedings of the 5th WSEAS International Conference on Applied Computer Science, Hangzhou, China, April 16-18, 2006 (pp ) Multiobjective Electricity Power Dispatch Using Multiobjective Particle Swarm Optimization Hongwen Yan, Rui Ma Changsha University of Science and Technology Chiling Road 45, Changsha, 40076 China Abstract:

More information

Particle Swarm Optimization Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm

Particle Swarm Optimization Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm Particle Swarm Optimization Artificial Bee Colony Chain (PSOABCC): A Hybrid Meteahuristic Algorithm Oğuz Altun Department of Computer Engineering Yildiz Technical University Istanbul, Turkey oaltun@yildiz.edu.tr

More information

Parameter Estimation of PI Controller using PSO Algorithm for Level Control

Parameter Estimation of PI Controller using PSO Algorithm for Level Control Parameter Estimation of PI Controller using PSO Algorithm for Level Control 1 Bindutesh V.Saner, 2 Bhagsen J.Parvat 1,2 Department of Instrumentation & control Pravara Rural college of Engineering, Loni

More information

Effectual Multiprocessor Scheduling Based on Stochastic Optimization Technique

Effectual Multiprocessor Scheduling Based on Stochastic Optimization Technique Effectual Multiprocessor Scheduling Based on Stochastic Optimization Technique A.Gowthaman 1.Nithiyanandham 2 G Student [VLSI], Dept. of ECE, Sathyamabama University,Chennai, Tamil Nadu, India 1 G Student

More information

Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization

Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization Lana Dalawr Jalal Abstract This paper addresses the problem of offline path planning for

More information

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM

CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 20 CHAPTER 2 CONVENTIONAL AND NON-CONVENTIONAL TECHNIQUES TO SOLVE ORPD PROBLEM 2.1 CLASSIFICATION OF CONVENTIONAL TECHNIQUES Classical optimization methods can be classified into two distinct groups:

More information

Solving Hard Multiobjective Problems with a Hybridized Method

Solving Hard Multiobjective Problems with a Hybridized Method Solving Hard Multiobjective Problems with a Hybridized Method Leticia C. Cagnina and Susana C. Esquivel LIDIC (Research Group). Universidad Nacional de San Luis Ej. de Los Andes 950 - (5700) San Luis,

More information

SwarmOps for Matlab. Numeric & Heuristic Optimization Source-Code Library for Matlab The Manual Revision 1.0

SwarmOps for Matlab. Numeric & Heuristic Optimization Source-Code Library for Matlab The Manual Revision 1.0 Numeric & Heuristic Optimization Source-Code Library for Matlab The Manual Revision 1.0 By Magnus Erik Hvass Pedersen November 2010 Copyright 2009-2010, all rights reserved by the author. Please see page

More information

Particle Swarm Optimization to Solve Optimization Problems

Particle Swarm Optimization to Solve Optimization Problems Particle Swarm Optimization to Solve Optimization Problems Gregorio Toscano-Pulido and Carlos A. Coello Coello Evolutionary Computation Group at CINVESTAV-IPN (EVOCINV) Electrical Eng. Department, Computer

More information

Evolutionary multi-objective algorithm design issues

Evolutionary multi-objective algorithm design issues Evolutionary multi-objective algorithm design issues Karthik Sindhya, PhD Postdoctoral Researcher Industrial Optimization Group Department of Mathematical Information Technology Karthik.sindhya@jyu.fi

More information

Multi-Objective Memetic Algorithm using Pattern Search Filter Methods

Multi-Objective Memetic Algorithm using Pattern Search Filter Methods Multi-Objective Memetic Algorithm using Pattern Search Filter Methods F. Mendes V. Sousa M.F.P. Costa A. Gaspar-Cunha IPC/I3N - Institute of Polymers and Composites, University of Minho Guimarães, Portugal

More information

EE 553 Term Project Report Particle Swarm Optimization (PSO) and PSO with Cross-over

EE 553 Term Project Report Particle Swarm Optimization (PSO) and PSO with Cross-over EE Term Project Report Particle Swarm Optimization (PSO) and PSO with Cross-over Emre Uğur February, 00 Abstract In this work, Particle Swarm Optimization (PSO) method is implemented and applied to various

More information

KEYWORDS: Mobile Ad hoc Networks (MANETs), Swarm Intelligence, Particle Swarm Optimization (PSO), Multi Point Relay (MPR), Throughput.

KEYWORDS: Mobile Ad hoc Networks (MANETs), Swarm Intelligence, Particle Swarm Optimization (PSO), Multi Point Relay (MPR), Throughput. IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY APPLICATION OF SWARM INTELLIGENCE PSO TECHNIQUE FOR ANALYSIS OF MULTIMEDIA TRAFFIC AND QOS PARAMETERS USING OPTIMIZED LINK STATE

More information

Feeder Reconfiguration Using Binary Coding Particle Swarm Optimization

Feeder Reconfiguration Using Binary Coding Particle Swarm Optimization 488 International Journal Wu-Chang of Control, Wu Automation, and Men-Shen and Systems, Tsai vol. 6, no. 4, pp. 488-494, August 2008 Feeder Reconfiguration Using Binary Coding Particle Swarm Optimization

More information

Open Access A Novel Multi-objective Network Recommender Algorithm Based on PSO

Open Access A Novel Multi-objective Network Recommender Algorithm Based on PSO Send Orders for Reprints to reprints@benthamscience.ae The Open Automation and Control Systems Journal, 05, 7, 949-955 949 Open Access A Novel Multi-objective Network Recommender Algorithm Based on PSO

More information

Small World Network Based Dynamic Topology for Particle Swarm Optimization

Small World Network Based Dynamic Topology for Particle Swarm Optimization Small World Network Based Dynamic Topology for Particle Swarm Optimization Qingxue Liu 1,2, Barend Jacobus van Wyk 1 1 Department of Electrical Engineering Tshwane University of Technology Pretoria, South

More information

Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques

Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques Hybrid Particle Swarm-Based-Simulated Annealing Optimization Techniques Nasser Sadati Abstract Particle Swarm Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly

More information

Two Heuristic Operations to Improve the Diversity of Two-objective Pareto Solutions

Two Heuristic Operations to Improve the Diversity of Two-objective Pareto Solutions Two Heuristic Operations to Improve the Diversity of Two-objective Pareto Solutions Rinku Dewri and Darrell Whitley Computer Science, Colorado State University Fort Collins, CO 80524 {rinku,whitley}@cs.colostate.edu

More information

Optimal Power Flow Using Particle Swarm Optimization

Optimal Power Flow Using Particle Swarm Optimization Optimal Power Flow Using Particle Swarm Optimization M.Chiranjivi, (Ph.D) Lecturer Department of ECE Bule Hora University, Bulehora, Ethiopia. Abstract: The Optimal Power Flow (OPF) is an important criterion

More information

Multi-objective Optimization Algorithm based on Magnetotactic Bacterium

Multi-objective Optimization Algorithm based on Magnetotactic Bacterium Vol.78 (MulGrab 24), pp.6-64 http://dx.doi.org/.4257/astl.24.78. Multi-obective Optimization Algorithm based on Magnetotactic Bacterium Zhidan Xu Institute of Basic Science, Harbin University of Commerce,

More information

Finding Sets of Non-Dominated Solutions with High Spread and Well-Balanced Distribution using Generalized Strength Pareto Evolutionary Algorithm

Finding Sets of Non-Dominated Solutions with High Spread and Well-Balanced Distribution using Generalized Strength Pareto Evolutionary Algorithm 16th World Congress of the International Fuzzy Systems Association (IFSA) 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) Finding Sets of Non-Dominated Solutions with High

More information

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES

CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES CHAPTER 6 HYBRID AI BASED IMAGE CLASSIFICATION TECHNIQUES 6.1 INTRODUCTION The exploration of applications of ANN for image classification has yielded satisfactory results. But, the scope for improving

More information

Particle Swarm Optimization For N-Queens Problem

Particle Swarm Optimization For N-Queens Problem Journal of Advanced Computer Science and Technology, 1 (2) (2012) 57-63 Science Publishing Corporation www.sciencepubco.com/index.php/jacst Particle Swarm Optimization For N-Queens Problem Aftab Ahmed,

More information

Index Terms PSO, parallel computing, clustering, multiprocessor.

Index Terms PSO, parallel computing, clustering, multiprocessor. Parallel Particle Swarm Optimization in Data Clustering Yasin ORTAKCI Karabuk University, Computer Engineering Department, Karabuk, Turkey yasinortakci@karabuk.edu.tr Abstract Particle Swarm Optimization

More information

A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEM TO MAXIMIZING PRODUCTION. Received March 2013; revised September 2013

A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEM TO MAXIMIZING PRODUCTION. Received March 2013; revised September 2013 International Journal of Innovative Computing, Information and Control ICIC International c 2014 ISSN 1349-4198 Volume 10, Number 2, April 2014 pp. 729 740 A DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM

More information

International Conference on Modeling and SimulationCoimbatore, August 2007

International Conference on Modeling and SimulationCoimbatore, August 2007 International Conference on Modeling and SimulationCoimbatore, 27-29 August 2007 OPTIMIZATION OF FLOWSHOP SCHEDULING WITH FUZZY DUE DATES USING A HYBRID EVOLUTIONARY ALGORITHM M.S.N.Kiran Kumara, B.B.Biswalb,

More information

Adaptative Clustering Particle Swarm Optimization

Adaptative Clustering Particle Swarm Optimization Adaptative Clustering Particle Swarm Optimization Salomão S. Madeiro, Carmelo J. A. Bastos-Filho, Member, IEEE, and Fernando B. Lima Neto, Senior Member, IEEE, Elliackin M. N. Figueiredo Abstract The performance

More information

Generation of Ultra Side lobe levels in Circular Array Antennas using Evolutionary Algorithms

Generation of Ultra Side lobe levels in Circular Array Antennas using Evolutionary Algorithms Generation of Ultra Side lobe levels in Circular Array Antennas using Evolutionary Algorithms D. Prabhakar Associate Professor, Dept of ECE DVR & Dr. HS MIC College of Technology Kanchikacherla, AP, India.

More information

Improving Tree-Based Classification Rules Using a Particle Swarm Optimization

Improving Tree-Based Classification Rules Using a Particle Swarm Optimization Improving Tree-Based Classification Rules Using a Particle Swarm Optimization Chi-Hyuck Jun *, Yun-Ju Cho, and Hyeseon Lee Department of Industrial and Management Engineering Pohang University of Science

More information

OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE

OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE OPTIMUM CAPACITY ALLOCATION OF DISTRIBUTED GENERATION UNITS USING PARALLEL PSO USING MESSAGE PASSING INTERFACE Rosamma Thomas 1, Jino M Pattery 2, Surumi Hassainar 3 1 M.Tech Student, Electrical and Electronics,

More information

PARTICLE SWARM OPTIMIZATION (PSO) [1] is an

PARTICLE SWARM OPTIMIZATION (PSO) [1] is an Proceedings of International Joint Conference on Neural Netorks, Atlanta, Georgia, USA, June -9, 9 Netork-Structured Particle Sarm Optimizer Considering Neighborhood Relationships Haruna Matsushita and

More information

Design optimization method for Francis turbine

Design optimization method for Francis turbine IOP Conference Series: Earth and Environmental Science OPEN ACCESS Design optimization method for Francis turbine To cite this article: H Kawajiri et al 2014 IOP Conf. Ser.: Earth Environ. Sci. 22 012026

More information

ARMA MODEL SELECTION USING PARTICLE SWARM OPTIMIZATION AND AIC CRITERIA. Mark S. Voss a b. and Xin Feng.

ARMA MODEL SELECTION USING PARTICLE SWARM OPTIMIZATION AND AIC CRITERIA. Mark S. Voss a b. and Xin Feng. Copyright 2002 IFAC 5th Triennial World Congress, Barcelona, Spain ARMA MODEL SELECTION USING PARTICLE SWARM OPTIMIZATION AND AIC CRITERIA Mark S. Voss a b and Xin Feng a Department of Civil and Environmental

More information

Small World Particle Swarm Optimizer for Global Optimization Problems

Small World Particle Swarm Optimizer for Global Optimization Problems Small World Particle Swarm Optimizer for Global Optimization Problems Megha Vora and T.T. Mirnalinee Department of Computer Science and Engineering S.S.N College of Engineering, Anna University, Chennai,

More information

QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION

QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION International Journal of Computer Engineering and Applications, Volume VIII, Issue I, Part I, October 14 QUANTUM BASED PSO TECHNIQUE FOR IMAGE SEGMENTATION Shradha Chawla 1, Vivek Panwar 2 1 Department

More information

Modified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update

Modified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update Modified Particle Swarm Optimization with Novel Modulated Inertia for Velocity Update Abdul Hadi Hamdan #1, Fazida Hanim Hashim #2, Abdullah Zawawi Mohamed *3, W. M. Diyana W. Zaki #4, Aini Hussain #5

More information

A RANDOM SYNCHRONOUS-ASYNCHRONOUS PARTICLE SWARM OPTIMIZATION ALGORITHM WITH A NEW ITERATION STRATEGY

A RANDOM SYNCHRONOUS-ASYNCHRONOUS PARTICLE SWARM OPTIMIZATION ALGORITHM WITH A NEW ITERATION STRATEGY A RANDOM SYNCHRONOUS-ASYNCHRONOUS PARTICLE SWARM OPTIMIZATION ALORITHM WITH A NEW ITERATION STRATEY Nor Azlina Ab Aziz 1,2, Shahdan Sudin 3, Marizan Mubin 1, Sophan Wahyudi Nawawi 3 and Zuwairie Ibrahim

More information

Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach Richard Allmendinger,XiaodongLi 2,andJürgen Branke University of Karlsruhe, Institute AIFB, Karlsruhe, Germany 2 RMIT University,

More information

Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design

Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design TELKOMNIKA, Vol. 11, No. 6, June 2013, pp. 2926 ~ 2932 e-issn: 2087-278X 2926 Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design Xuesong Yan* 1, Qinghua Wu 2,3, Hammin

More information

Model Parameter Estimation

Model Parameter Estimation Model Parameter Estimation Shan He School for Computational Science University of Birmingham Module 06-23836: Computational Modelling with MATLAB Outline Outline of Topics Concepts about model parameter

More information

PARALLEL PARTICLE SWARM OPTIMIZATION IN DATA CLUSTERING

PARALLEL PARTICLE SWARM OPTIMIZATION IN DATA CLUSTERING PARALLEL PARTICLE SWARM OPTIMIZATION IN DATA CLUSTERING YASIN ORTAKCI Karabuk University, Computer Engineering Department, Karabuk, Turkey E-mail: yasinortakci@karabuk.edu.tr Abstract Particle Swarm Optimization

More information

Performance Evaluation of Vector Evaluated Gravitational Search Algorithm II

Performance Evaluation of Vector Evaluated Gravitational Search Algorithm II 170 New Trends in Software Methodologies, Tools and Techniques H. Fujita et al. (Eds.) IOS Press, 2014 2014 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-61499-434-3-170 Performance

More information

Tracking Changing Extrema with Particle Swarm Optimizer

Tracking Changing Extrema with Particle Swarm Optimizer Tracking Changing Extrema with Particle Swarm Optimizer Anthony Carlisle Department of Mathematical and Computer Sciences, Huntingdon College antho@huntingdon.edu Abstract The modification of the Particle

More information

Multiprocessor Scheduling Using Particle Swarm Optimization

Multiprocessor Scheduling Using Particle Swarm Optimization S.N.Sivanandam 1, P Visalakshi 2, and A.Bhuvaneswari 3 Professor and Head 1 Senior Lecturer 2 Pg Student 3 Department of Computer Science and Engineering, Psg College of Technology, Peelamedu, Coimbatore

More information

PARTICLE Swarm Optimization (PSO), an algorithm by

PARTICLE Swarm Optimization (PSO), an algorithm by , March 12-14, 2014, Hong Kong Cluster-based Particle Swarm Algorithm for Solving the Mastermind Problem Dan Partynski Abstract In this paper we present a metaheuristic algorithm that is inspired by Particle

More information

Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller

Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No, September ISSN (Online): 694-84 www.ijcsi.org 5 Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller

More information

GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization

GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization Stephen M. Majercik 1 1 Department of Computer Science, Bowdoin College, Brunswick, Maine, USA smajerci@bowdoin.edu Keywords: Abstract:

More information

Swarm Intelligence Particle Swarm Optimization. Erick Luerken 13.Feb.2006 CS 790R, University of Nevada, Reno

Swarm Intelligence Particle Swarm Optimization. Erick Luerken 13.Feb.2006 CS 790R, University of Nevada, Reno Swarm Intelligence Particle Swarm Optimization Erick Luerken 13.Feb.2006 CS 790R, University of Nevada, Reno Motivation Discuss assigned literature in terms of complexity leading to actual applications

More information

FDR PSO-Based Optimization for Non-smooth Functions

FDR PSO-Based Optimization for Non-smooth Functions M. Anitha, et al. / International Energy Journal 0 (009) 37-44 37 www.serd.ait.ac.th/reric FDR PSO-Based Optimization for n-smooth Functions M. Anitha*, S. Subramanian* and R. Gnanadass + Abstract In this

More information

A Hybrid Fireworks Optimization Method with Differential Evolution Operators

A Hybrid Fireworks Optimization Method with Differential Evolution Operators A Fireworks Optimization Method with Differential Evolution Operators YuJun Zheng a,, XinLi Xu a, HaiFeng Ling b a College of Computer Science & Technology, Zhejiang University of Technology, Hangzhou,

More information

SIMULTANEOUS COMPUTATION OF MODEL ORDER AND PARAMETER ESTIMATION FOR ARX MODEL BASED ON MULTI- SWARM PARTICLE SWARM OPTIMIZATION

SIMULTANEOUS COMPUTATION OF MODEL ORDER AND PARAMETER ESTIMATION FOR ARX MODEL BASED ON MULTI- SWARM PARTICLE SWARM OPTIMIZATION SIMULTANEOUS COMPUTATION OF MODEL ORDER AND PARAMETER ESTIMATION FOR ARX MODEL BASED ON MULTI- SWARM PARTICLE SWARM OPTIMIZATION Kamil Zakwan Mohd Azmi, Zuwairie Ibrahim and Dwi Pebrianti Faculty of Electrical

More information

Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems

Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems Hongbo Liu 1,2,AjithAbraham 3,1, Okkyung Choi 3,4, and Seong Hwan Moon 4 1 School of Computer

More information

Using Different Many-Objective Techniques in Particle Swarm Optimization for Many Objective Problems: An Empirical Study

Using Different Many-Objective Techniques in Particle Swarm Optimization for Many Objective Problems: An Empirical Study International Journal of Computer Information Systems and Industrial Management Applications ISSN 2150-7988 Volume 3 (2011) pp.096-107 MIR Labs, www.mirlabs.net/ijcisim/index.html Using Different Many-Objective

More information

Improving local and regional earthquake locations using an advance inversion Technique: Particle swarm optimization

Improving local and regional earthquake locations using an advance inversion Technique: Particle swarm optimization ISSN 1 746-7233, England, UK World Journal of Modelling and Simulation Vol. 8 (2012) No. 2, pp. 135-141 Improving local and regional earthquake locations using an advance inversion Technique: Particle

More information

THREE PHASE FAULT DIAGNOSIS BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM

THREE PHASE FAULT DIAGNOSIS BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM THREE PHASE FAULT DIAGNOSIS BASED ON RBF NEURAL NETWORK OPTIMIZED BY PSO ALGORITHM M. Sivakumar 1 and R. M. S. Parvathi 2 1 Anna University, Tamilnadu, India 2 Sengunthar College of Engineering, Tamilnadu,

More information

Lamarckian Repair and Darwinian Repair in EMO Algorithms for Multiobjective 0/1 Knapsack Problems

Lamarckian Repair and Darwinian Repair in EMO Algorithms for Multiobjective 0/1 Knapsack Problems Repair and Repair in EMO Algorithms for Multiobjective 0/ Knapsack Problems Shiori Kaige, Kaname Narukawa, and Hisao Ishibuchi Department of Industrial Engineering, Osaka Prefecture University, - Gakuen-cho,

More information

POWER FLOW OPTIMIZATION USING SEEKER OPTIMIZATION ALGORITHM AND PSO

POWER FLOW OPTIMIZATION USING SEEKER OPTIMIZATION ALGORITHM AND PSO POWER FLOW OPTIMIZATION USING SEEKER OPTIMIZATION ALGORITHM AND PSO VIGNESH.P Department of Electrical & Electronics Engineering Anna University Veerammal Engineering College, Dindigul, Tamilnadu India

More information

Adaptive Radiation Pattern Optimization for Antenna Arrays by Phase Perturbations using Particle Swarm Optimization

Adaptive Radiation Pattern Optimization for Antenna Arrays by Phase Perturbations using Particle Swarm Optimization 2010 NASA/ESA Conference on Adaptive Hardware and Systems Adaptive Radiation Pattern Optimization for Antenna Arrays by Phase Perturbations using Particle Swarm Optimization Virgilio Zuniga, Ahmet T. Erdogan,

More information

Automatic differentiation based for particle swarm optimization steepest descent direction

Automatic differentiation based for particle swarm optimization steepest descent direction International Journal of Advances in Intelligent Informatics ISSN: 2442-6571 Vol 1, No 2, July 2015, pp. 90-97 90 Automatic differentiation based for particle swarm optimization steepest descent direction

More information

An Evolutionary Algorithm Approach to Generate Distinct Sets of Non-Dominated Solutions for Wicked Problems

An Evolutionary Algorithm Approach to Generate Distinct Sets of Non-Dominated Solutions for Wicked Problems An Evolutionary Algorithm Approach to Generate Distinct Sets of Non-Dominated Solutions for Wicked Problems Marcio H. Giacomoni Assistant Professor Civil and Environmental Engineering February 6 th 7 Zechman,

More information

A genetic algorithms approach to optimization parameter space of Geant-V prototype

A genetic algorithms approach to optimization parameter space of Geant-V prototype A genetic algorithms approach to optimization parameter space of Geant-V prototype Oksana Shadura CERN, PH-SFT & National Technical Univ. of Ukraine Kyiv Polytechnic Institute Geant-V parameter space [1/2]

More information

Parallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resources

Parallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resources Parallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resources Sanaz Mostaghim, Jürgen Branke, Andrew Lewis, Hartmut Schmeck Abstract In this paper, we study parallelization

More information

DEMO: Differential Evolution for Multiobjective Optimization

DEMO: Differential Evolution for Multiobjective Optimization DEMO: Differential Evolution for Multiobjective Optimization Tea Robič and Bogdan Filipič Department of Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia tea.robic@ijs.si

More information

Structural Damage Detection by Multi-objective Intelligent algorithm Algorithm

Structural Damage Detection by Multi-objective Intelligent algorithm Algorithm Structural Damage Detection by Multi-objective Intelligent algorithm Algorithm Y.W. Wang, Q. Ma & W. Li Institute of Engineering Mechanics, China Earthquake Administration, China SUMMARY: In order to solve

More information

SwarmOps for Java. Numeric & Heuristic Optimization Source-Code Library for Java The Manual Revision 1.0

SwarmOps for Java. Numeric & Heuristic Optimization Source-Code Library for Java The Manual Revision 1.0 Numeric & Heuristic Optimization Source-Code Library for Java The Manual Revision 1.0 By Magnus Erik Hvass Pedersen June 2011 Copyright 2009-2011, all rights reserved by the author. Please see page 4 for

More information