PSO_Bounds: A New Hybridization Technique of PSO and EDAs

Particle Swarm Optimization (PSO) is a nature inspired population-based approach successfully used as an optimization tool in many application. Estimation of distribution algorithms (EDAs), are evolutionary algorithms that try to estimate the probability distribution of the good individuals in the population. In this work, we present a new PSO algorithm that borrows ideas from EDAs. This algorithm is implemented and compared to previous PSO and EDAs hybridization approaches using a suite of well-known benchmark optimization functions.

[1]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[2]  Louise Travé-Massuyès,et al.  Telephone Network Traffic Overloading Diagnosis and Evolutionary Computation Techniques , 1997, Artificial Evolution.

[3]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[4]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[5]  J. A. Lozano,et al.  Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing) , 2006 .

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[9]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[10]  Marco Antonio Montes de Oca,et al.  An Estimation of Distribution Particle Swarm Optimization Algorithm , 2006, ANTS Workshop.

[11]  Michèle Sebag,et al.  Extending Population-Based Incremental Learning to Continuous Search Spaces , 1998, PPSN.

[12]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[13]  Marcus Gallagher,et al.  Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator , 1999, GECCO.

[14]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[15]  Kenneth A. De Jong,et al.  Artificial Evolution , 2021, Lecture Notes in Computer Science.

[16]  Jose Miguel Puerta,et al.  Initial approaches to the application of islands-based parallel EDAs in continuous domains , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).