Solving Engineering Design Problems by Social Cognitive Optimization

Swarm systems are products of natural evolution. The complex collective behavior can emerge from a society of N autonomous cognitive entities [2], called as agents [5]. Each agent acquires knowledge in socially biased individual learning [4]. For human, the extrasomatic arbitrary symbols that manipulated by language allows for cognition on a grand scale [3], since agent can acquire social information that is no longer limited to direct observation to other agents. The individual learning then only plays secondary role due to the ubiquity and efficiency of social learning [1].

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

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

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

[4]  P. Niyogi,et al.  Computational and evolutionary aspects of language , 2002, Nature.

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

[6]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[7]  Dearborn.  Animal Intelligence: An Experimental Study of the Associative Processes in Animals , 1900 .

[8]  Nancy J. Nersessian,et al.  Understanding the Creative Mind: A Review of Margaret Boden's Creative Mind , 1995, Artif. Intell..

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

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

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

[12]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[13]  A. Bandura.  Social cognitive theory: an agentic perspective. , 1999, Annual review of psychology.

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

[15]  Xiao-Feng Xie,et al.  Social cognitive optimization for nonlinear programming problems , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

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

[17]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

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

[19]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[20]  A. Ram,et al.  Understanding the Creative Mind , 1995 .

[21]  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.