MRMOGA: parallel evolutionary multiobjective optimization using multiple resolutions

Whereas multiobjective evolutionary algorithms have reached certain effectiveness in solving many real-world problems efficiency still remains as an open problem. One choice to reduce the execution time of the multiobjective evolutionary algorithms is their parallelization. This paper introduces a parallel MOEA which is based on the island model with heterogeneous nodes. This algorithm is characterized by encoding the solutions using a different resolution for each island. In this way, the search space is divided into well-defined overlapped regions in decision variable space.

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

[2]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[3]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  A. Osyczka,et al.  A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm , 1995 .

[6]  Alan H. Karp,et al.  Measuring parallel processor performance , 1990, CACM.

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

[8]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[9]  Andreas Zell,et al.  Parallelization of Multi-objective Evolutionary Algorithms Using Clustering Algorithms , 2005, EMO.

[10]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

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

[12]  Kalyanmoy Deb,et al.  Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms , 2003, EMO.

[13]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[14]  Enrique Alba,et al.  Parallel evolutionary algorithms can achieve super-linear performance , 2002, Inf. Process. Lett..

[15]  Tomoyuki Hiroyasu,et al.  The new model of parallel genetic algorithm in multi-objective optimization problems - divided range multi-objective genetic algorithm , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  J. Branke,et al.  Guidance in evolutionary multi-objective optimization , 2001 .

[17]  Kwong-Sak Leung,et al.  Asynchronous self-adjustable island genetic algorithm for multi-objective optimization problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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