A dynamic migration policy to the Island Model

The Island Model is a mechanism that promotes improvement in the quality of the results produced by evolutionary algorithms and speed up their executions. A reason of the impact caused by the Island Model in the quality of results is the migration of solutions between islands that occurs periodically during the search process. The migration process depends on decisions such as the choice of solutions that will be send, the destination islands etc. This set of decisions is known as migration policy. This paper proposes a migration policy to the Island Model in which the destination island for an emigrant solution is defined according to the attractiveness of the islands in the model. In the proposed model the attractiveness between islands also influences the connection between them and affect the topology of the model. This paper evaluated if the proposed model is able to maintain the two main characteristics of the Island Model. The movement of solutions and the states of the connections were evaluated too.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Dervis Karaboga,et al.  Artificial bee colony algorithm for large-scale problems and engineering design optimization , 2012, J. Intell. Manuf..

[3]  Omar Al Jadaan,et al.  IMPROVED SELECTION OPERATOR FOR GA 1 , 2008 .

[4]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .

[6]  Dario Izzo,et al.  On the impact of the migration topology on the Island Model , 2010, Parallel Comput..

[7]  Helio J. C. Barbosa,et al.  Migration policies to improve exploration in parallel island models for optimization via metaheuristics , 2015 .

[8]  Dervis Karaboga,et al.  On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation , 2015, Inf. Sci..

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[11]  John H. Holland,et al.  Genetic Algorithms and the Optimal Allocation of Trials , 1973, SIAM J. Comput..

[12]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999, Complex..

[13]  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.

[14]  Frederico G. Guimarães,et al.  Dynamic selection of migration flows in island model differential evolution , 2013, GECCO.

[15]  Zbigniew Skolicki,et al.  An analysis of island models in evolutionary computation , 2005, GECCO '05.

[16]  Dario Izzo,et al.  The asynchronous island model and NSGA-II: study of a new migration operator and its performance , 2013, GECCO '13.

[17]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[18]  Dario Izzo,et al.  Parallel global optimisation meta-heuristics using an asynchronous island-model , 2009, 2009 IEEE Congress on Evolutionary Computation.

[19]  Carlos A. Coello Coello,et al.  A comparative study of differential evolution variants for global optimization , 2006, GECCO.

[20]  Teodor Gabriel Crainic,et al.  Parallel Strategies for Meta-Heuristics , 2003, Handbook of Metaheuristics.

[21]  Ulrich Bodenhofer,et al.  Genetic Algorithms: Theory and Applications , 2002 .

[22]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[23]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[24]  Victor O. K. Li,et al.  A social spider algorithm for global optimization , 2015, Appl. Soft Comput..

[25]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..