Engineering design optimization using species-conserving genetic algorithms

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.

[1]  P. John Clarkson,et al.  Multimodal Function Optimization Using Species Conservation , 2001 .

[2]  Xiaodong Li,et al.  Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization , 2004, GECCO.

[3]  Lahbib Chibani,et al.  Optimum Design of Structures , 1989 .

[4]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[5]  Xiaodong Li,et al.  Efficient differential evolution using speciation for multimodal function optimization , 2005, GECCO '05.

[6]  P. John Clarkson,et al.  Complexity analysis of evaluating distances in the genetic algorithm using species conservation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[7]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[8]  D. Goldberg,et al.  Adaptive Niching via coevolutionary Sharing , 1997 .

[9]  Li Jianping,et al.  The effect of distance measure in a GA with species conservation , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[11]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[12]  Yuval Davidor,et al.  A Naturally Occurring Niche and Species Phenomenon: The Model and First Results , 1991, ICGA.

[13]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[14]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[15]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[16]  Jacques Periaux,et al.  Genetic Algorithms in Engineering and Computer Science , 1996 .

[17]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[18]  D. J. Cavicchio,et al.  Adaptive search using simulated evolution , 1970 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .