Particle Swarm Optimization


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1 Dario Schor, M.Sc., EIT Space Systems Department Magellan Aerospace Winnipeg Winnipeg, Manitoba 1 of 34
2 Optimization Techniques Motivation Optimization: Where, min x F(x), subject to g(x) = 0 g(x) represents constraints on the parameters. F(x) knowledge: With prior knowledge, we can select appropriate techniques to find a solution quickly and efficiently. E.g., Using gradient based techniques of unimodal functions. Without prior knowledge, the parameter space is too large to evaluate all options, so heuristics are needed to find good solutions efficiently. 2 of 34
3 Motivation (PSO) Evolutionary algorithm modeled after schools of fish and flocks of birds. Iterative algorithm where particles move through a parameter space looking for a good solution. At each step, the movement is based on a weighted sum between the personal knowledge of a particle and the collective knowledge of the swarm. 3 of 34
4 Verifying Algorithms Motivation Test Functions There are many different kinds of evolutionary algorithms like genetic algorithms, simulated annealing, ant colony optimization, and PSO. What s different in each one? There are many different implementations for a single algorithm; How do you compare the different implementations? How do you pick which implementation to use? A common approach is to use a set of benchmark functions (i.e., Sphere, Rastrigin, Griewank, and Rosenbrock). 4 of 34
5 A Study of PSO Trajectories for RealTime Scheduling Outline Motivation Original algorithm Some variations Demonstration Simulation in 2D using NetLogo Experiments Benchmark functions Concluding Remarks 5 of 34
6 Original PSO Algorithm (1 of 2) Model behavior of schools of fish and birds [KeEb95] A set of S K particles are randomly distributed within the parameter space. The particles traverse through a Kdimensional space looking for an optimal solution. At each iterations, n, the particles update their position based on their personal and social knowledge. 6 of 34
7 Original PSO Algorithm (2 of 2) Movement of particles based on [KeEb01] S v,k S x,k ( n) = S v,k ( n 1) + S ϕ1 random(0,1) S p,k S x,k ( n 1) ( ) + S ϕ 2 random(0,1) S p,g S x,k ( n) = S x,k ( n 1) + S v,k ( n) ( n 1) ( ) Update velocity (1) Personal influence (2) Social influence (3) Update position (4) where, S x,k, S v,k, S p,k S ϕ1, S ϕ 2 S p,g Position, velocity, and best position Personal, and social weights Best position in neighbourhood 7 of 34
8 Original PSO Algorithm (2 of 2) The steps are updated independently for each of the K dimensions in the problem. The step is determined based on: 1. Update velocity This serves as an inertial force that encourages particles to continue moving in the direction they were already going. 2. Personal influence Memory of the particle from previous individual best solution found. 3. Social influence Memory and knowledge from the community/swarm. 4. Update position Increments the current position along one dimension. 8 of 34
9 Original PSO Algorithm Implementation Initialization: Randomly select starting position and velocities. Usually constrained to a range based on empirical testing. 9 of 34
10 Original PSO Algorithm Implementation Main loop: Result of velocity equation. Result of position equation. 10 of 34
11 PSO Variations Population Size Intuitively: More particles = greater exploration of the parameter space. More particles = more resources to execute algorithm. Recommended sizes in literature are [20, 100] Requires testing within the specified problem space to evaluate. Directly linked to the type of topology used. 11 of 34
12 PSO Variations Neighborhood Topologies Social knowledge derived from neighborhood topology used [ScKA10] Divided into global (gbest) and local (lbest) topologies gbest can converge faster, but may get stuck in a local minimum gbest lbest ring lbest star lbest torus lbest hierarchical 12 of 34
13 PSO Variations Limiting Step Size Required to prevent the velocity from growing too quickly. The compounded step size could cause particles to diverge from the rest of the swarm. Some options: 1. Limit velocities using hardcoded values Effective, but can lead to particles oscillating around the solution. Ideally, we want the step sizes to decrease as we approach the solution. Picked based on expected range for solutions. 13 of 34
14 PSO Variations Limiting Step Size Some options continued: 2. Use an inertial weight Like a temperature gradient in simulated annealing. Decreases a weight multiplier for the velocity from 0.9 to 0.4. Equation for the decrease is usually nonlinear exponential decay. Although used in late 1990 s, this is seldom used today as it introduces yet another parameter to vary in the problem. Furthermore, it is more susceptible to getting stuck in local solutions. 14 of 34
15 PSO Variations Limiting Step Size Some options continued: 3. Using a constriction coefficient Derived from the eigenvalues such that they are real and nonnegative guaranteeing convergence. Commonly used in most PSO implementations. 15 of 34
16 PSO Variations Personal and Social Weights Initially picked at random in the range 0 to 1. Intuitively it is like any person: Sometimes you have your own plans, and sometimes you go along with your friends and do something else. A multiplier was added to help particles move faster towards the solution. 16 of 34
17 PSO Variations Stopping Criterion Different options: Solution found satisfies problem constrains. All particles converge on an region. Best solution does not change for a few iterations in a row. 17 of 34
18 Demonstration NetLogo Multiagent programmable modeling environment. Developed by Dr. Uri Wilensky from Northwestern University. Software used in courses throughout North America such as the Santa Fe Institute. Free to download from: 18 of 34
19 Demonstration NetLogo Allows you to create: GUI to test parameters in a simulation. Quick and easy to edit. Follows the 1980 s Logo principles where a Turtle moves throughout the screen. 19 of 34
20 Test Functions Benchmark Functions In 1975, De Jong proposed a set of test functions for characterizing and comparing the performance of different algorithms and their implementations. The set of functions grown since then to incorporate other important features representative of real problems. E.g., Lots of local solutions, fast/slow gradients, deep peaks, etc. These functions: Have known optimal values for comparison with calculated solution; Are nonlinear and often multimodal; Do not have constraints on the variables; and The nominal trajectories of the particles will differ as they approach the solution. 20 of 34
21 Benchmark Functions Sphere Function Simplest equation in the set. All points are monotonically decreasing towards the origin. Given by equation: Smooth and often used for comparing faster approaches. It is almost like testing a worst case sorted list with a sorting algorithm to see whether your algorithm is still smart enough to handle it. 21 of 34
22 Benchmark Functions Sphere Function 22 of 34
23 Benchmark Functions Sphere Trajectories Particles oscillate towards the solution [ScKi10] [ScKi11] Two distinct behaviors: Transient Steady state 23 of 34
24 Benchmark Functions Sphere Trajectories Select particle to study Fix particle initial conditions and run algorithm 200 times Nominal particle selected to minimize mean square error Position Position Position Position trajectory for S k =7 of 200 runs on G 1 (x) function Position 20 trajectory for S k =7 of 200 runs on G 1 (x) function Zoomed in Zoomed on typical in Iteration on and trajectories worst behaviour Zoomed in on typical and worst behaviour % CI Iteration Iteration Iteration Position Trajectories Mean 95% CI Typical (run 91) Worst (run 6) Traject Mean 95% C 95% Trajectories CI Typical Mean (run 91) Worst 95% (run CI6) 24 of 34
25 Benchmark Functions Rastrigin Function Given by equation: Shows envelope function that resembles the convex shape of the sphere to guide global search. Many local solutions through the cosine function. Usually implemented in a small range (5.12, 5.12). 25 of 34
26 Benchmark Functions Rastrigin Function 26 of 34
27 Benchmark Functions Rastrigin Trajectories Particles oscillate towards the solution [ScKi10], [ScKi11]. Two distinct behaviors: Transient Steady state 27 of 34
28 Benchmark Functions Rastrigin Trajectories Select particle to study Fix particle initial conditions and run algorithm 200 times Nominal particle selected to minimize mean square error Position trajectory for Sk=7 of 200 runs on G1(x) function 1 Traject Mean 95% C Position Position trajectory for Sk=7 of 200 runs on G1(x) function PositionPosition of Zoomed initeration on trajectories Iteration Iteration Trajectories Mean 95% CI Trajectorie Mean 95% CI
29 Benchmark Functions More Benchmarks Rosenbrock Smooth curves with mix of steep and gentle gradients. The gentle gradient makes it hard for some implementations. E.g., Using an inertia weight to decrease the step size over time. From: 29 of 34
30 Benchmark Functions More Benchmarks Ackley Lots of local minima to get trapped without a significant global gradient to guide search. Once you enter the big peak, you find the answer very quickly. From: 30 of 34
31 Benchmark Functions More Benchmarks List arranged by type: Has equation, graph, nominal range, and a description of the functions. List of common functions: Has equation, graph, nominal range, description, and MATLAB code for each function. Hedar_files/TestGO_files/Page364.htm 31 of 34
32 Benchmark Functions Picking Benchmarks Selection of benchmarks depends on: Common functions used in the literature for the algorithm you are testing. Ultimately, you want to compare your implementation/variation to the stateoftheart. Realproblem you are trying to solve (what is really representative) These are two separate objectives: 1 st points at Comp Sci analysis of algorithms, and 2 nd points to Engineering solution. It is a balancing act for selecting parameters. 32 of 34
33 Concluding Remarks Particle Swarm is a powerful algorithm for solving unconstrained optimization problems. Can be used for a variety of applications. Benchmark functions provide a standardized comparison between implementations and variations of the algorithm. 33 of 34
34 References [KeEb01] [KeEb95] James Kennedy and Russell Eberhart, Swarm Intelligence, Morgan Kaufmann, 2001, 512pp. {ISBN } James Kennedy and Russell Eberhart, Particle swarm optimization, in Proc. of the IEEE International Conference on Neural Networks, 1995, (Perth, WA, USA; November 27December 1, 1995), vol. 4, pp , [Scho13] Dario Schor, A Study of Trajectories for RealTime Scheduling, MSc. Thesis. University of Manitoba, [ScKA10] [ScKi10] [ScKi11] Dario Schor, Witold Kinsner, and John Anderson, A Study of Optimal Topologies in Swarm Intelligence, in Proc. of the IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2010, (Calgary, AB; May 2{5, 2010), pp. 1 8, Dario Schor and Witold Kinsner, A Study of for Cognitive Machines, in Proc. of the 9th IEEE Intermational Conference on Cognitive Informatics, ICCI 2010, (Beijin, China; July 7 9, 2010), pp , Dario Schor and Witold Kinsner, Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines," International Journal of Cognitive Informatics and Natural Intelligence, vol. 5, no. 1, pp , of 34
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