Chapter 14 SWARM INTELLIGENCE

The complex and often coordinated behavior of swarms fascinates not only biologists but also computer scientists. Bird flocking and fish schooling are impressive examples of coordinated behavior that emerges without central control. Social insect colonies show complex problemsolving skills arising from the actions and interactions of nonsophisticated individuals. Swarm Intelligence is a field of computer science that designs and studies efficient computational methods for solving problems in a way that is inspired by the behavior of real swarms or insect colonies (see e.g. Bonabeau et al., 1999; Kennedy et al., 2001). Principles of selforganization and local or indirect communication are important for understanding the complex collective behavior (Camazine et al., 2001). Examples where insights into the behavior of natural swarms has influenced the design of algorithms and systems in computer science include the following (see Bonabeau et al., 1999; Middendorf, 2002 for more information):

[1]  J. Kennedy.  Stereotyping: improving particle swarm performance with cluster analysis , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[2]  M. Dorigo,et al.  Aco Algorithms for the Traveling Salesman Problem , 1999 .

[3]  R. Pfeifer,et al.  A mobile robot employing insect strategies for navigation , 2000, Robotics Auton. Syst..

[4]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  T. Stützle,et al.  A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends , 2002 .

[7]  Hartmut Schmeck,et al.  Ant colony optimization for resource-constrained project scheduling , 2000, IEEE Trans. Evol. Comput..

[8]  Antonella Carbonaro,et al.  Ant Colony Optimization: An Overview , 2002 .

[9]  Andries Petrus Engelbrecht,et al.  Cooperative learning in neural networks using particle swarm optimizers , 2000, South Afr. Comput. J..

[10]  Walter J. Gutjahr,et al.  A Graph-based Ant System and its convergence , 2000, Future Gener. Comput. Syst..

[11]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[12]  Tiago Ferra de Sousa,et al.  Swarm optimisation as a new tool for data mining , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[13]  Russell C. Eberhart,et al.  Gene clustering using self-organizing maps and particle swarm optimization , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[14]  J. S. Vesterstrom,et al.  Division of labor in particle swarm optimisation , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[16]  Christian Blum,et al.  When Model Bias Is Stronger than Selection Pressure , 2002, PPSN.

[17]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[18]  Rodney M. Goodman,et al.  A scalable, distributed algorithm for allocating workers in embedded systems , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[19]  Thomas Stützle,et al.  A SHORT CONVERGENCE PROOF FOR A CLASS OF ACO ALGORITHMS , 2002 .

[20]  Daniel Merkle,et al.  On solving permutation scheduling problems with ant colony optimization , 2005, Int. J. Syst. Sci..

[21]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[22]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[23]  Pramod K. Varshney,et al.  Adaptive multimodal biometric fusion algorithm using particle swarm , 2003, SPIE Defense + Commercial Sensing.

[24]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[25]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[26]  Daniel Merkle,et al.  On the Behaviour of ACO Algorithms : Studies on Simple Problems , 2001 .

[27]  Vittorio Maniezzo,et al.  Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem , 1999, INFORMS J. Comput..

[28]  Maja J. Matarić,et al.  Robust Behavior-Based Control for Distributed Multi-Robot Collection Tasks , 2000 .

[29]  Joaquín Bautista,et al.  Ant Algorithms for Assembly Line Balancing , 2002, Ant Algorithms.

[30]  Michael Guntsch,et al.  Applying Population Based ACO to Dynamic Optimization Problems , 2002, Ant Algorithms.

[31]  Frans van den Bergh,et al.  A NICHING PARTICLE SWARM OPTIMIZER , 2002 .

[32]  H. Yoshida,et al.  A particle swarm optimization for reactive power and voltage control considering voltage security assessment , 1999, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[33]  Michael Sampels,et al.  Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[34]  Martin Middendorf,et al.  A Population Based Approach for ACO , 2002, EvoWorkshops.

[35]  Risto Miikkulainen,et al.  Adaptive Control Utilising Neural Swarming , 2002, GECCO.

[36]  Suganthan.  [IEEE 1999. Congress on Evolutionary Computation-CEC99 - Washington, DC, USA (6-9 July 1999)] Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) - Particle swarm optimiser with neighbourhood operator , 1999 .

[37]  Iain D. Couzin,et al.  Self‐Organization in Biological Systems.Princeton Studies in Complexity. ByScott Camazine,, Jean‐Louis Deneubourg,, Nigel R Franks,, James Sneyd,, Guy Theraulaz, and, Eric Bonabeau; original line drawings by, William Ristineand, Mary Ellen Didion; StarLogo programming by, William Thies. Princeton (N , 2002 .

[38]  Daniel Merkle,et al.  Ant Colony Optimization with the Relative Pheromone Evaluation Method , 2002, EvoWorkshops.

[39]  Thomas Stützle,et al.  An Experimental Study of the Simple Ant Colony Optimization Algorithm , 2001 .

[40]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[41]  T. Krink,et al.  Particle swarm optimisation with spatial particle extension , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[42]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[43]  Russell C. Eberhart,et al.  The particle swarm: social adaptation in information-processing systems , 1999 .

[44]  Marcus Randall,et al.  Intensification and diversification strategies in ant colony system , 2002 .

[45]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[46]  Thomas Stützle,et al.  Improvements on the Ant-System: Introducing the MAX-MIN Ant System , 1997, ICANNGA.

[47]  Marcus Randall,et al.  Anti-pheromone as a Tool for Better Exploration of Search Space , 2002, Ant Algorithms.

[48]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

[49]  Richard F. Hartl,et al.  An ant colony optimization approach for the single machine total tardiness problem , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[50]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .

[51]  Russell C. Eberhart,et al.  Swarm intelligence for permutation optimization: a case study of n-queens problem , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[52]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[53]  Walter J. Gutjahr,et al.  ACO algorithms with guaranteed convergence to the optimal solution , 2002, Inf. Process. Lett..

[54]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[55]  Eric Bonabeau,et al.  Cooperative transport by ants and robots , 2000, Robotics Auton. Syst..

[56]  Julia Handl,et al.  Improved Ant-Based Clustering and Sorting , 2002, PPSN.

[57]  Martin Middendorf,et al.  Modeling the Dynamics of Ant Colony Optimization , 2002, Evolutionary Computation.

[58]  Peter J. Bentley,et al.  Dynamic Search With Charged Swarms , 2002, GECCO.

[59]  Masahito Yamamoto,et al.  Multiple Ant Colonies Algorithm Based on Colony Level Interactions , 2000 .