In addition to hybrid swarm intelligence algorithms, another way for an swarm intelligence algorithm to have balance between an swarm intelligence

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1 xiv Preface Swarm intelligence algorithms are a collection of population-based stochastic optimization algorithms which are generally categorized under the big umbrella of evolutionary computation algorithms. There is no standard definition that defines a swarm intelligence algorithm and differentiates it from other population-based optimization algorithms. In the literature, there are a lot of swarm intelligence algorithms which have been reported and introduced, for example, ant colony optimization, artificial immune system, bacterial foraging optimization algorithm, bee colony optimization algorithm, brain storm optimization algorithm, firefly optimization algorithm, firework optimization algorithm, fish school search optimization algorithm, particle swarm optimization algorithm, shuffled frog-leaping algorithm, to name just a few. Generally speaking, a swarm intelligence algorithm is a population-based stochastic optimization algorithm that is inspired or motivated by the collective behavior of small and simple objects such as the ants in ant colony optimization algorithm, fishes in fish school search optimization algorithms, and birds in particle swarm optimization algorithm. Collectively and cooperatively, a population of individuals in a swarm intelligence algorithm, each of which represents a possible solution to the problem to be solved, is updated or generated iteration over iteration in the hope that the better and better population of individuals will be generated over iterations and finally a good enough solution will be found. Originally, a swarm optimization algorithm usually was designed or introduced to be simple in concept which was at least one reason that the swarm intelligence algorithm attracted attentions initially. With the successes and wide studies of a swarm intelligence algorithm, it has been and will be applied to solve more and more complicated problems which are at least difficult, if not impossible, for traditional algorithms such as hill-climbing algorithms to solve. As a consequence of solving more complicated optimization problems, the swarm intelligence algorithm itself has been under extensive modifications and studies. One tendency of researches on swarm intelligence algorithms is to combine an original or a modified swarm intelligence algorithm with another optimization algorithm in the hope that the hybrid algorithm can be better than any component algorithm itself in general or in solving one specific problem. One common goal of a hybrid algorithm is to have one component algorithm focus on global search while another component algorithm focuses on local search so that the hybrid algorithm has better balance between exploration capability and exploitation capability and therefore has more capability to avoid premature convergence and to find better and good enough solution(s). One example is the complementary cyber swarm intelligence introduced in the chapter A Complementary Cyber Swarm Algorithm in this book. This is also one of common purposes of researches on memetic algorithms which, in the aspects of swarm intelligence researches, combine a swarm intelligence algorithm with another swarm intelligence algorithm or one of other non-swarm intelligence algorithms.

2 In addition to hybrid swarm intelligence algorithms, another way for an swarm intelligence algorithm to have balance between an swarm intelligence algorithm s exploration capability and exploitation capability is to design the swarm intelligence algorithm to be able to converge to a local minimum or to diverge from a local minimum when it is necessary. For example, a swarm intelligence algorithm needs to have the capability to converge so that the swarm intelligence algorithm can converge to (or find) a good enough solution. But for complicated, nonlinear, especially multimodal problems, it is easy for an swarm intelligence algorithm to be trapped into a local minimum, therefore, the swarm intelligence algorithm needs to be able to jump out of the local minimum, that is, to diverge from the local minimum so that the swarm intelligence algorithm can start another round of convergent search process in the hope to find a better and good enough solution. One way to design a swarm intelligence algorithm to be able in either convergent search process or divergent search process is to establish a relationship between at least one parameter of the swarm intelligence algorithm and the search process of the swarm intelligence algorithm. For example, for a parameter of an swarm intelligence algorithm, if the parameter is within one range of values, the search process of the swarm intelligence algorithm is in convergent search process while in another range of values, the search process is in divergent search process, then the swarm intelligence algorithm can be put into either convergent or divergent search process by simply changing the value of the parameter. A good example is the chaos-enhanced firefly algorithm proposed in the chapter Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning. Another way is to design a swarm intelligence algorithm to have both convergent operation and divergent operation during each iteration so that the search process of the swarm intelligence algorithm involves both convergent search operation and divergent search operation; therefore, intuitively, the swarm intelligence algorithm has higher potential to escape from local minima which represent not-good enough solutions. A good example is the brain storm optimization algorithm discussed in the chapter An Optimization Algorithm Based on Brainstorming Process in this book. Another trend for the research on swarm intelligence algorithms is to specially design or modify a swarm intelligence algorithm to be able to solve one kind of optimization problems more effectively and/ or more efficiently. For example, for the multi-objective optimization problems with constraints, special care could and should be taken for particle swarm optimization algorithms to solve them, such as how to take care of feasible and infeasible solutions. Another example is to use an optimization algorithm to solve optimization problems with boundary constraints. Special techniques have to be designed and implemented to take care of boundary constraints violations. Also, certain performance metrics could be defined and measured to monitor the search process of the optimization algorithm, such as the population diversity defined in particle swarm optimization algorithms. One major purpose of the research on swarm intelligence algorithms is to be able to apply these swarm intelligence algorithms to solve real-world problems. So far, swarm intelligence algorithms have been applied to successfully solve wide range of application problems. Successful applications of these swarm intelligence algorithms are the major vitality to keep the researches on swarm intelligence algorithms to be active and attract attentions from wider and wider range of applications. Therefore, the application of swarm intelligence algorithms to effectively and efficiently solve real-world problems is another important and critical research direction on swarm intelligence in addition to the research direction on swarm intelligence algorithms. xv

3 xvi WHAT IS THE BOOK ABOUT? This book volume does not intend to cover all aspects of research on swarm intelligence algorithms and their applications. It is a snapshot of current research on swarm intelligence algorithms and their applications which are included and/or covered in the 2011 issues of the International Journal of Swarm Intelligence Research. It may reflect a research tendency in the research areas of swarm intelligence algorithms. This book volume can be a good reference book for researchers who have been conducting research work on swarm intelligence algorithms or at least are interested in the research areas of swarm intelligence algorithms. It can also be used as a reference book for graduate students and senior undergraduate students who are going to conduct researches on swarm intelligence algorithms and/or their applications. ORGANIZATION OF THE BOOK This book volume consists of 14 chapters which are organized into two sections for the convenience of reference. Section 1 includes 8 chapters which are about current research works on swarm intelligence algorithms. Section 2 includes 6 chapters which are about the applications of swarm intelligence algorithms. Section 1: Swarm Intelligence Algorithms Scatter search algorithm was introduced by Glover in It is a population-based search algorithm originally designed for solving general integer problems. The scatter search algorithm can be implemented as an integration of five basic components, which are: diversification-generation component, improvement component, reference-set update component, subset-generation component, and solution-combination component. The reference-set is usually a smaller subset of the population of individuals with better qualities, and plays a central role in the algorithm. Path relinking was also introduced by Glover in It used the neighborhood space instead of Euclidean space in scatter search. The search algorithms can be considered as forming path between and/or beyond existing solutions. In the chapter Scatter Search and Path Relinking: A Tutorial on the Linear Arrangement Problem, Marti et al. reviewed the scatter search algorithm and path relinking process, and applied them to solve the minimum linear arrangement problem, which is a NP-hard combinatorial optimization problem, in order to illustrate how the algorithms work. The authors also discussed the relationship between scatter search/path relinking, and the recent particle swarm optimization algorithm introduced by Kennedy and Eberhart in 1995 through pointing out the differences and similarities between them. Hybrid algorithms combine one algorithm with another algorithm by taking advantage of strength of all involved algorithms. A cyber swarm algorithm is a combination of particle swarm optimization algorithm with scatter search algorithm embedded with path relinking process. It intends to make the particle swarm optimization algorithm more effective than those without combing scatter search with path relinking process by better balancing between intensification and diversification. In the chapter A Complementary Cyber Swarm Intelligence, Yin et al. proposed a complementary cyber swarm algorithm which makes use a different set of ideas from the Tabu search in addition to the scatter search

4 with path relinking process. It can better utilize the history search information to guide or restrict the search for better exploitation of the search area and for better manipulation of adaptive memory which consists of best solutions observed throughout search process derived from Tabu search. Experimental results demonstrated that the proposed complementary cyber swam algorithm performs better than the original cyber swarm intelligence and the standard PSO proposed by Clerc at least with regards to the benchmark functions tested. Max-cut problem is a NP-hard optimization problem. Its objective is to find a cut in a graph which has the maximum sum of the edge weights. Path relinking method involves a pair of solutions, an initiating solution and a guiding solution, and generates a set of solutions that lie on a path between or beyond the pair of solutions. Global equilibrium search algorithm generates a set of initial solutions at each different temperature value, which is similar to that in the simulated annealing method, for other local search methods as starting solutions. In the Chapter Path Relinking Scheme for the Max-Cut Problem within Global Equilibrium Search, Shylo and Shylo propose a hybrid search algorithm which embeds the path relinking method into the global equilibrium search algorithm to take advantages from both approaches, that is, maintaining high quality of solutions by the global equilibrium search algorithm, and combining existing high quality solutions to form a new enhanced solution by path relinking method. The proposed hybrid algorithm was applied to solve max-cut problem. Experimental results show that the proposed approach can provide solutions with better quality within less time. In the chapter Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem, James and Rego proposed a hybrid optimization algorithm for solving quadratic assignment problem. Their proposed algorithm is a combination of path relinking algorithm and the Tabu search method. Path relinking algorithm forms a path between an initiating solution and one or more guiding solutions by selecting high quality solutions from a set of best solutions obtained in the history of search process, which is similar to the concept of personal, local, and/or global best positions in a particle swarm optimization algorithm. Tabu search is a neighborhood search technique which can provide higher quality solutions from a starting solution. By selecting different tabu restriction and aspiration criteria, the Tabu search method can make a trade-off between intensification (exploitation) and diversification (exploration). In this chapter, the authors utilized a Tabu search with multi-starts as the improvement method component in the path relinking algorithm to further improve the solutions on the path between an initiating solution and guiding solutions. The outcome solutions with better quality from the Tabu search in return will be used as new initiating and guiding solutions by the path relinking methods. The proposed algorithm was tested on the quadratic assignment problem (QAP) which is a classical NP-hard combinatorial optimization problem. In addition, the authors analyzed different strategies and their effectiveness on solving QAP problems with different landscapes, and therefore provided a guidance to integrate these components to design more effective QAP algorithms. The authors also compared the proposed algorithm with the particle swarm optimization algorithm and pointed out their differences and similarities. For most real world multi-objective optimization problems, there are constraints which define the feasibility of potential solutions. The only feasible solutions are acceptable solutions, and infeasible solutions are those which need to be avoided. There are several existing techniques to handle constraints. For example, 1) higher priority is given to constraints so that feasible solution areas will more likely be searched compared with infeasible solution areas; 2) genetic operators are designed to allow only feasible solutions surviving into next generation; 3) dominance principles are defined to rank all individuals including both feasible and infeasible solutions so that higher rank solutions will be preferred over generations; et cetera. These existing constraints handling techniques have been adopted by multi- xvii

5 xviii objective particle swarm optimization (MOPSO) algorithms to handle multi-objective problems with constraints. In the chapter A Multiobjective Particle Swarm Optimizer for Constrained Optimization, the authors proposed to convert constraints into one extra objective the purpose of which is to have zero constraint violation after going through an evolutionary process. For example, a k-objective optimization problem with constraints will be converted into a (k+1)-objective optimization problem without constraints. The additional objective is defined as the constraint violation which is then a minimization objective function. The essential goals of the proposed MOPSO are 1) to search for feasible solutions through guiding obtained infeasible solutions towards feasible solutions over generations; 2) to converge to feasible optimal solution or Pareto front eventually. In order to achieve the above goals, the proposed constrained MOPSO modified a standard MOPSO in the following aspects, 1) a rank-constraint violation indicator and a feasibility ration are defined for the step of updating particles personal best memory; 2) two fixed size archives are designed to hold feasible global best solutions and infeasible global best solutions, respectively so that the infeasible solutions can be used as bridges to explore feasible solution areas; 3) the global best is selected equally from the above two archives and by applying tournament selection with the use of dynamic crowing distance values; 4) the particles are updated by taking consideration of the constraint violation and the feasibility ratio; 5) applying both uniform mutation and Gaussian mutation operations. Simulation results on the benchmark functions illustrated that the proposed MOPSO is highly competitive by comparing with other three existing algorithms, i.e., NSGA- II, GZHW, and WTY algorithms. When a particle swarm optimization algorithm applies to solve a multimodal problem, premature convergence may happen. The designs of various particles swarm optimization algorithms for multimodal problems have to take and have taken this into consideration. This consideration may be also necessary for designing particle swarm optimization algorithms for unimodal problems. For example, for a unimodal problem with boundary constraints, premature convergence may occur if boundary constraints are handled improperly. In the chapter Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization: From Population Diversity Perspective Cheng et al. experimentally studied boundary constraints handling techniques in particle swarm optimization algorithms with different topology structures. In this chapter, population diversity, which is a way to measure the distribution of particles in the search space, is utilized to monitor the search process of particle swarm optimization algorithms with different topology structures and different boundary handling techniques. The topology structure of a particle swarm optimization algorithm determines the information propagation method and speed. A good particle swarm optimization algorithm generally should possess a good balance between its exploration capability and exploitation capability over its entire search process. The topology structure of a particle swarm optimization algorithm determines the information propagation method and speed, therefore different boundary handling technique may be required for a particle swarm optimization algorithm with a different topology structure. The topology structures studied in the chapter include star structure, ring structure, four cluster structure, and von Neumann structure. The boundary constraints handling techniques studied include classical strategy, deterministic strategy, stochastic strategy, and modified stochastic strategy. Experimental results and observations revealed the tendency of particles exploration capability and exploitation capability during the search process. For example, a deterministic boundary handling technique may improve the search performance of the particle swarm optimization algorithm with ring, four clusters, or Von Neumann structure, but not the star structure; Stochastic boundary handling technique can have good exploration capability, and therefore, by further including the method of resetting particles in a small or decreased region, the particle swarm optimization algo-

6 rithm will also retain good exploitation ability so that better performance can be achieved by the particle swarm optimization algorithm. In general, from the search tendency revealed by the population diversity, a more effective and efficient particle swarm optimization algorithm could be designed for solving an optimization problem by considering, say, boundary handling technique and topology structure together. Firefly optimization algorithm is a population-based algorithm which is inspired by flashing behavior of fireflies. Like other swarm intelligence algorithms, premature convergence may occur in firefly optimization algorithm, and firefly optimization algorithm may be trapped in any local minima which is not a good enough solution to the problem to be solved. A critical feature for an optimization algorithm to have is to have the capability to escape from local minimum, therefore to have more chances to search other local minima and eventually find at least a local minima, if not a global optima, which represents a good enough solution to the problem to be solved. Traditionally, an optimization algorithm is designed to have the ability to avoid instability or chaotic behavior. In the chapter Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning, Yang analyzed the original firefly optimization algorithm and observed that by changing a parameter of the simplified firefly algorithm s equation with a single agent (individual), chaotic behavior can be observed. It is then reasonable to believe that chaotic behavior should appear in the original firefly optimization algorithm by changing its parameter. As a consequence, the firefly optimization algorithm can enter either a stable and convergent search process or a chaotic and divergent search process by intensively controlling or adjusting this parameter, therefore, the firefly optimization algorithm can be modified to interchangeably enter either convergent search process or divergent search process. That means at least theoretically the firefly optimization algorithm can be made to balance between its exploration capability and exploitation capability. In the chapter, the author further discussed the automatic parameter tuning for the proposed chaos-enhanced firefly optimization algorithm. There are a lot of swarm intelligence algorithms reported in the literature so far, such as particle swarm optimization algorithm, ant colony optimization algorithm, fish school search optimization algorithm, bacterial foraging optimization algorithm, firework optimization algorithm, and firefly optimization algorithm, to name just a few. Most of these swarm intelligence algorithms are inspired by objects with low level intelligence at individual level, for example, birds in particle swarm optimization algorithm, ants in ant colony optimization algorithm, fishes in fish school search optimization algorithm, et cetera. The collective collaboration of these objects with low level intelligence inspired the designs of these optimization algorithms, which have shown good search capability. It is natural and intuitive to expect that an optimization algorithm inspired by the collective collaboration of human being should be at least competitive with, if not superior to, these swarm intelligence algorithms inspired by the collective collaboration of these objects with low level intelligence, because human being are the most intelligent animal in this world. In the chapter An Optimization Algorithm Based on Brainstorming Process, Shi proposed a new population-based stochastic optimization algorithm called the brain storm optimization algorithm, which is inspired by the human being brainstorming process. Brainstorming process is often utilized by a group of people to brain storm good ideas to solve problems that is very difficult, if not impossible, for a single person to solve. In the chapter, Shi first introduced one type of brainstorming process, which is then modeled. The brainstorming process model is then standardized and abstracted as a flow chart based on which two versions of brain storm optimization algorithms are designed and implemented. A brain storm optimization algorithm basically consists of two kind major operations within each iteration: the clustering operation and the individual updating operation. The clustering operation focuses more on convergence of the search process while the individual generation operation focuses xix

7 xx more on divergence of search process. In other words, each iteration of the brain storm optimization algorithm involves both expansion operation and contraction operation. The two versions of brain storm optimization algorithms are tested on ten benchmark functions. The experimental results validated and illustrated the effectiveness and efficiency of the proposed brain storm optimization algorithms. Furthermore, in the chapter, two measurement metrics are defined: the average inter-cluster distance and the inter-cluster diversity. The average inter-cluster distance measures and/or monitors the distribution of cluster centers, while the inter-cluster diversity can be looked as a measurement of information entropy for the population of individuals in the brain storm optimization algorithm. The two metrics can be further utilized to monitor and control the search process of a brain storm optimization algorithm so that better performed brain storm optimization algorithm can be achieved. Section 2: Swarm Intelligence Applications In applications such as text mining, web classification, etc., there are huge amount of data items which have been and will be continually collected. Furthermore, each data item represents a point in a highdimensional space. For example, in web classification, each web can be represented by a vector of occurrence frequencies of all words that are possible to occur in any web page. These high-dimensional data vectors together form a data matrix which usually is a sparse matrix. Therefore, for the web classification problem, a good way is to greatly reduce its dimension through low-rank approximation approaches. Most commonly used low rank approximation approaches are singular value decomposition, principal component analysis, factor analysis, independent components analysis, and multidimensional scaling, to name just a few. Among them, singular value decomposition and principal component analysis approaches provide best approximation in the sense that Frobenius norm is the smallest. However, the low rank approximations obtained by singular value decomposition and principal component analysis contain elements with both positive and negative values. For many applications, negative values in a low rank approximation are not meaningful; therefore, a low rank approximation with all non-negative elements are sought, called non-negative matrix factorization (NMF). One goal of NMF as in the web classification is to reduce dimensionality so that the classification task can be easier to achieve, and at the same time, the classification accuracy can be higher. Another more general goal of NMF is to find a good enough non-negative matrix approximate for large sparse matrix such as that generated in the web classification application which usually will be modeled as an optimization problem with nonlinear, no-convex, discontinuous, and multimodal objective function. The population-base algorithms such as swarm intelligence algorithms are good choices to solve this kind of optimization problems. In the chapter Swarm Intelligence for Non-Negative Matrix Factorization, Janecek and Tan utilized five different population-based algorithms as the NMF approaches, which are: particle swarm optimization algorithm, genetic algorithm, fish school search algorithm, differential evolution, and firework algorithm. Among them, three are swarm intelligence algorithms. To improve convergence speed and increase classification accuracy, two optimization strategies for initialization and iterative update are designed and implemented in the five population-based optimization algorithms. A watchman route problem (WRP) is a problem that is to find the shortest route in a polygon with holes under the condition that all points inside the polygon are visible from the route. The generalized watchman route problem is a problem that mobile robot operator should find the shortest route for the robot to oversee the whole area with holes. Both the WRP and generalized WAP problems are NP-hard problems. Ant colony optimization algorithm is a nature-inspired optimization algorithm that was ab-

8 stracted and modeled from what ants do. Simulated ants intend to find the shortest path by following the path that has the stronger smell of pheromone. In the chapter How Ants Can Efficiently Solve the Generalized Watchman Route Problem, Paduch and Sapiecha proposed to use ant colony optimization algorithm to solve generalized watchman route problem. Image segmentation is a problem to label every pixel of an image so that the image can be simplified or be changed into a different image which is more meaningful and easier to understand. Fuzzy C-Means algorithm is a clustering algorithm that is widely used for image segmentation. Bacterial foraging optimization algorithm is a stochastic optimization algorithm that is motivated by the bacterial foraging behavior of E. coli. In the chapter Image Segmentation Based on Bacterial Foraging and FCM Algorithm, Mo and Yin proposed a hybrid algorithm, which combines the bacterial foraging optimization algorithm and fuzzy c-means algorithm to solve the image segmentation problems. Image segmentation problem is first represented as an optimization problem. Then the fuzzy-c-means algorithm is used to solve the optimization problem, and the bacterial foraging optimization algorithm is used to reinforce the global search capability of fuzzy c-means algorithm so that it can overcome the original fuzzy c-means algorithm s poor global search capability, and therefore can avoid its converging into local minima. The proposed hybrid algorithm is tested on several image segmentation problems. Experimental results illustrated the good performance of the proposed algorithm. Frequency assignment problem (FAP) is a problem to assign frequency to transmitters in a wireless communication network with at most acceptable interference among channels. Due to the limited available radio spectrum, FAP is a difficult, challenging, and practical problem. FAP will usually be represented as a strongly NP-hard optimization problem which optimization algorithms are designed to solve. In the chapter Minimum Span Frequency Assignment Based on a Multiagent Evolutionary Algorithm, Liu et al. proposed a hybrid algorithm, which is a combination of multi-agent system and evolutionary algorithm, to solve minimum span FAP problems the objective of which is to minimize the required frequency spectrum to meet a given level of reception quality over the network. Agents live in a lattice-like environment while evolutionary algorithm is utilized to evolve the agents behaviors so that to increase their energies, which correspond to better frequency assignment with minimum span. The proposed algorithm was tested on T-coloring problems, which is very closely related to the FAP problems, with different sizes and Philadelphia benchmark for FAPs. Experimental results illustrated the good performance of the proposed algorithm. The control of a nuclear reactor usually is based on data from a set of sensors. In order to control the nuclear system, which can be modeled as a nonlinear system, the control system needs to be reliable, robust, and stable, which means the control system needs to be a redundant control system in order to be able to detect and react to sensors failures quickly. One way to add redundancy is using hardware redundancy, for example, with redundant sensors. One possible problem with the redundant sensor strategy is that the lifetime expectation for all sensors is similar, therefore with one sensor fails, the other sensors are also like to fail soon. The other way is to add detect system to detect any possible failure and react to the detected failure. In the chapter Design of Robust Approach for Failure Detection in Dynamic Control Systems, Zaki Ei-Far proposed a robust instrument fault detection (IFD) algorithm based on a modified immune mechanism based evolutionary algorithm. Based on the proposed IFD algorithm, a fault diagnosis logic system is further created on the purpose to be able to determine on line optimal control action, quick fault detection, and controller structure reconfigurations. Through a sampling time, cycles of genetic operators are performed to ensure the capability of the proposed algorithm. The proposed IFD algorithm is applied to the control system of a nonlinear nuclear power reactor to illustrate the xxi

9 xxii performance of the proposed algorithm. Experimental results showed that the designed fault diagnosis logic system is capable of detecting the fault and reconfiguring the controller system under situations with system parameters uncertainties and noisy data. In applications such as surveillance operations, automated lawn mowing, and automated vacuum cleaning, distributed area coverage using multi-robot system is an important research topic in which multiple robots are teamed up to cover and/or explore an environment which may be initially unknown. Numerous area coverage algorithms exist to solve this kind of coverage problems in which usually, the entire environment should be covered or explored by at least one robot. For most existing coverage techniques, they consider the coverage problem independently, for example, each robot performs its action individually, while in reality, there are numerous different scenarios out there. For example, robots may be equipped with different sensors, and therefore, have different functionalities. Several robots with different functionalities should team up to perform one single task. Therefore for this kind of task, a more efficient solution is to consider the coverage problem together with the multi-robot formation problem. In the chapter Effects of Multi-Robot Team Formations on Distributed Area Coverage, Dasgupta, et al. combined the multi-robot formation techniques and area coverage techniques together so that an initially unknown environment can be efficiently covered by maintaining effective team formation of multiple robots. The authors theoretically analyzed their approach and conducted extensive experiments on the Webots robotic simulation platform in addition to using physical robots within an indoor environment. Yuhui Shi Xi an Jiaotong-Liverpool University, China