Society and civilization: An optimization algorithm based on the simulation of social behavior

The ability to mutually interact is a fundamental social behavior in all human and insect societies. Social interactions enable individuals to adapt and improve faster than biological evolution based on genetic inheritance alone. This is the driving concept behind the optimization algorithm introduced in this paper that makes use of the intra and intersociety interactions within a formal society and the civilization model to solve single objective constrained optimization problems. A society corresponds to a cluster of points in the parametric space while a civilization is a set of all such societies. Every society has its set of better performing individuals (leaders) that help others to improve through information exchange. This results in the migration of a point toward a better performing point, analogous to an intensified local search. Leaders improve only through an intersociety information exchange that results in the migration of a leader from a society to another. This helps the better performing societies to expand and flourish.

[1]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[2]  R. K. Ursem.  Multinational evolutionary algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  L. Darrell Whitley,et al.  Optimization Using Distributed Genetic Algorithms , 1990, PPSN.

[4]  Michèle Sebag,et al.  Controlling Crossover through Inductive Learning , 1994, PPSN.

[5]  R. Reynolds,et al.  Using knowledge-based evolutionary computation to solve nonlinear constraint optimization problems: a cultural algorithm approach , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[6]  K. Deb,et al.  Real-coded evolutionary algorithms with parent-centric recombination , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[8]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[9]  Li Chen,et al.  TAGUCHI-AIDED SEARCH METHOD FOR DESIGN OPTIMIZATION OF ENGINEERING SYSTEMS , 1998 .

[10]  Wojciech Marks,et al.  Multiobjective structural optimization , 1986 .

[11]  Michèle Sebag,et al.  An Advanced Evolution Should Not Repeat its Past Errors , 1996, ICML.

[12]  Tapabrata Ray,et al.  ENGINEERING DESIGN OPTIMIZATION USING A SWARM WITH AN INTELLIGENT INFORMATION SHARING AMONG INDIVIDUALS , 2001 .

[13]  S. Kodiyalam,et al.  Geometry and optimization techniques for structural design , 1994 .

[14]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[15]  Joachim Born,et al.  Modelling and Simulation of Distributed Evolutionary Search Processes for Function Optimization , 1990, Parallel Problem Solving from Nature.

[16]  James N. Siddall,et al.  Analytical decision-making in engineering design , 1972 .

[17]  Zbigniew Michalewicz,et al.  A Survey of Constraint Handling Techniques in Evolutionary Computation Methods , 1995 .

[18]  K. M. Ragsdell,et al.  Optimal Design of a Class of Welded Structures Using Geometric Programming , 1976 .

[19]  Marco Laumanns,et al.  On the Effects of Archiving, Elitism, and Density Based Selection in Evolutionary Multi-objective Optimization , 2001, EMO.

[20]  Carlos A. Coello Coello,et al.  Self-adaptive penalties for GA-based optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[22]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[23]  Jeffrey Horn,et al.  Handbook of evolutionary computation , 1997 .

[24]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[25]  Panos Y. Papalambros,et al.  PRODUCTION SYSTEM FOR USE OF GLOBAL OPTIMIZATION KNOWLEDGE. , 1985 .

[26]  Tapabrata Ray,et al.  Golinski's speed reducer problem revisited , 2003 .

[27]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[28]  M. Sebag,et al.  A society of hill-climbers , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

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

[30]  Shapour Azarm,et al.  Multi-level design optimization using global monotonicity analysis , 1989 .

[31]  Michèle Sebag,et al.  Toward Civilized Evolution: Developing Inhibitions , 1997, ICGA.

[32]  Robert G. Reynolds,et al.  A Cultural Algorithm Framework to Evolve Multi-Agent Cooperation with Evolutionary Programming , 1997, Evolutionary Programming.

[33]  Shigenobu Kobayashi,et al.  A Real-Coded Genetic Algorithm for Function Optimization Using the Unimodal Normal Distribution Crossover , 1999 .

[34]  Kalyanmoy Deb,et al.  Optimal design of a welded beam via genetic algorithms , 1991 .