MRMOGA: a new parallel multi‐objective evolutionary algorithm based on the use of multiple resolutions

In this paper, we introduce MRMOGA (Multiple Resolution Multi‐Objective Genetic Algorithm), a new parallel multi‐objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well‐defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state‐of‐the‐art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi‐objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright © 2006 John Wiley & Sons, Ltd.

[1]  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).

[2]  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).

[3]  Tomoyuki Hiroyasu,et al.  NCGA: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems , 2002, GECCO Late Breaking Papers.

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

[5]  Ian C. Parmee,et al.  Co-operative Evolutionary Strategies for Single Component Design , 1997, ICGA.

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

[7]  Tong Heng Lee,et al.  Multiobjective Evolutionary Algorithms and Applications , 2005, Advanced Information and Knowledge Processing.

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

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

[10]  Alexander Korobov.  Planigon tessellation cellular automata , 1999 .

[11]  Tomoyuki Hiroyasu,et al.  LCGA: Local Cultivation Genetic Algorithm For Multi-objective Optimization Problems , 2002, GECCO.

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

[13]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

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

[15]  Ben Paechter,et al.  PSFGA : Parallel processing and evolutionary computation for multiobjective optimisation , 2004 .

[16]  Andrzej Osyczka,et al.  Evolutionary Algorithms for Single and Multicriteria Design Optimization , 2001 .

[17]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[18]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[19]  Gary B. Lamont,et al.  Considerations in engineering parallel multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[20]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[21]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

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

[23]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

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

[25]  Hajime Kita,et al.  Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm , 1996, PPSN.

[26]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[27]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[28]  Jeffrey Horn,et al.  Multicriterion decision making , 1997 .

[29]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

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

[31]  Carlos A. Coello Coello,et al.  A coevolutionary multi-objective evolutionary algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

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

[33]  C. Darwin.  On the Origin of Species by Means of Natural Selection: Or, The Preservation of Favoured Races in the Struggle for Life , 2019 .

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

[35]  Jason R. Schott.  Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. , 1995 .

[36]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[37]  Hidefumi Sawai,et al.  Parallel distributed processing of a parameter-free GA by using hierarchical migration methods , 1999 .

[38]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

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

[40]  David W. Corne,et al.  Properties of an adaptive archiving algorithm for storing nondominated vectors , 2003, IEEE Trans. Evol. Comput..

[41]  F Detoronegro.  PSFGA: Parallel processing and evolutionary computation for multiobjective optimisation , 2004 .

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

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

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

[45]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

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

[47]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[48]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.