A Specialized Island Model and Its Application in Multiobjective Optimization

This paper discusses a new model of parallel evolutionary algorithms (EAs) called the specialized island model (SIM) that can be used to generate a set of diverse non-dominated solutions to multiobjective optimization problems. This model is derived from the island model, in which an EA is divided into several subEAs that exchange individuals among them. In SIM, each subEA is responsible (i.e., specialized) for optimizing a subset of the objective functions in the original problem. The efficacy of SIM is demonstrated using a three-objective optimization problem. Seven scenarios of the model with a different number of subEAs, communication topology, and specialization are tested, and their results are compared. The results suggest that SIM effectively finds non-dominated solutions to multiobjective optimization problems.

[2]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[3]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[4]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[5]  Forum Mpi.  MPI: A Message-Passing Interface , 1994 .

[6]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

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

[8]  Theodore C. Belding,et al.  The Distributed Genetic Algorithm Revisited , 1995, ICGA.

[9]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[10]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[11]  Franciszek Seredynski,et al.  New Trends in Parallel and Distributed Evolutionary Computing , 1998, Fundam. Informaticae.

[12]  Enrique Alba,et al.  Parallelism and evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[13]  S. Griffis.  EDITOR , 1997, Journal of Navigation.

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

[15]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

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

[17]  Paul Bryant Grosso,et al.  Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model , 1985 .

[18]  Alex Rapaport,et al.  Mpi-2: extensions to the message-passing interface , 1997 .

[19]  Stéphane Lanteri,et al.  A two-level parallelization strategy for Genetic Algorithms applied to optimum shape design , 2000, Parallel Comput..

[20]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[21]  H. Fawcett.  Manual of Political Economy , 1995 .

[22]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[23]  Jack Dongarra,et al.  PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing , 1995 .

[24]  Erick Cantú-Paz,et al.  Efficient and Accurate Parallel Genetic Algorithms , 2000, Genetic Algorithms and Evolutionary Computation.

[25]  James P. Cohoon,et al.  C6.3 Island (migration) models: evolutionary algorithms based on punctuated equilibria , 1997 .

[26]  John J. Grefenstette,et al.  Genetic algorithms and their applications , 1987 .

[27]  J. Periaux,et al.  Parallel Genetic Solution for Multiobjective MDO , 1996, Parallel CFD.

[28]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.