An Ecology-Based Heterogeneous Approach for Cooperative Search

The concept of optimization is present in several natural processes such as the evolution of species, the behavior of social groups and the ecological relationships of different animal populations. This work uses the concepts of habitats, ecological relationships and ecological successions to build a hybrid cooperative search algorithm, named ECO. The Artificial Bee Colony (ABC) and the Particle Swarm Optimization (PSO) algorithms were used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC and PSO running alone, and with both algorithms in a well known island model with ring topology, all running without the ecology concepts previously mentioned. The ECO algorithm performed better than the other approaches, especially as the dimensionality of the functions increase, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations. Results suggest that the ECO algorithm can be an interesting alternative for numerical optimization.

[1]  Robert M. May,et al.  Theoretical Ecology: Principles and Applications , 1981 .

[2]  Rafael S. Parpinelli,et al.  An eco-inspired evolutionary algorithm applied to numerical optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[3]  José L. Verdegay,et al.  On the Performance of Homogeneous and Heterogeneous Cooperative Search Strategies , 2008, NICSO.

[4]  Belén Melián-Batista,et al.  Nature Inspired Cooperative Strategies for Optimization, NICSO 2008, Puerto de La Cruz, Tenerife, Spain, 12-14 November 2008 , 2009, NICSO.

[5]  Seth D. Guikema,et al.  A derivation of the number of minima of the Griewank function , 2008, Appl. Math. Comput..

[6]  Leandro Nunes de Castro,et al.  Fundamentals of natural computing: an overview , 2007 .

[7]  A. Griewank.  Generalized descent for global optimization , 1981 .

[8]  Rafael S. Parpinelli,et al.  Parallelism, hybridism and coevolution in a multi‐level ABC‐GA approach for the protein structure prediction problem , 2012, Concurr. Comput. Pract. Exp..

[9]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[10]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[11]  Rafael Stubs Parpinelli,et al.  Parallel Approaches for the Artificial Bee Colony Algorithm , 2011 .

[12]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[13]  Panos M. Pardalos,et al.  A Collection of Test Problems for Constrained Global Optimization Algorithms , 1990, Lecture Notes in Computer Science.

[14]  Yuhui Shi,et al.  Handbook of Swarm Intelligence: Concepts, Principles and Applications , 2011 .

[15]  M. Begon,et al.  Ecology: From Individuals to Ecosystems , 2005 .

[16]  Christian Blum,et al.  Hybrid Metaheuristics , 2010, Artificial Intelligence: Foundations, Theory, and Algorithms.

[17]  M. Kamel,et al.  A Taxonomy of Cooperative Search Algorithms , 2005, Hybrid Metaheuristics.

[18]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[19]  Konstantinos G. Margaritis,et al.  An Experimental Study of Benchmarking Functions for Genetic Algorithms , 2002, Int. J. Comput. Math..

[20]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[21]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..