SWAF: Swarm Algorithm Framework for Numerical Optimization

A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both of the macro rules by simple combination and subsymbolic deploying of multiple rules among them are also studied. Experimental results on benchmark problems are presented, and performance comparison between SWAF and other existing algorithms indicates that it is efficiently.

[1]  John R. Anderson.  ACT: A simple theory of complex cognition. , 1996 .

[2]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Thomas G. Dietterich.  Learning at the knowledge level , 2004, Machine Learning.

[4]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[5]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[6]  P. Bak,et al.  Learning from mistakes , 1997, Neuroscience.

[7]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[8]  Gerd Gigerenzer,et al.  Visions of rationality , 1998, Trends in Cognitive Sciences.

[9]  Emile H. L. Aarts,et al.  Global optimization and simulated annealing , 1991, Math. Program..

[10]  P M Todd,et al.  Précis of Simple heuristics that make us smart , 2000, Behavioral and Brain Sciences.

[11]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[12]  BENNETT G. GALEF Jr,et al.  Why behaviour patterns that animals learn socially are locally adaptive , 1995, Animal Behaviour.

[13]  C. Heyes,et al.  SOCIAL LEARNING IN ANIMALS: CATEGORIES AND MECHANISMS , 1994, Biological reviews of the Cambridge Philosophical Society.

[14]  D Cvijovicacute,et al.  Taboo search: an approach to the multiple minima problem. , 1995, Science.

[15]  P. Richerson,et al.  The Origin and Evolution of Cultures , 2005 .

[16]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[19]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[20]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

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

[22]  Jiming Liu,et al.  Multiagent diffusion and distributed optimization , 2003, AAMAS '03.

[23]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[24]  T. SHALLICE,et al.  Learning and Memory , 1970, Nature.

[25]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[26]  E. Thorndike.  “Animal Intelligence” , 1898, Nature.

[27]  John R. Anderson,et al.  A Theory of the Origins of Human Knowledge , 1989, Artif. Intell..

[28]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[29]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[30]  M. Flinn.  Culture and the evolution of social learning , 1997 .

[31]  P. Bak,et al.  Adaptive learning by extremal dynamics and negative feedback. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[33]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[34]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[35]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[36]  H. Simon,et al.  Invariants of human behavior. , 1990, Annual review of psychology.