A Novel Multi-Objective Evolutionary Algorithm Based on External Dominated Clustering

Evolutionary algorithms (EAs) have wide applications in practice and many advantages over traditional methods in solving nonlinear and complex optimal problems. In this paper, we propose a novel clustering technique, in which the infeasible solutions are employed to divide the feasible solutions into several clusters. There is no more one infeasible individual in each cluster. A novel evolutionary algorithm based on this technique called ED-MOEA is proposed for dealing with constrained multi-objective problems. Simulation results on five test problems indicate the proposed algorithm is effective.

[1]  Joshua D. Knowles,et al.  An Evolutionary Approach to Multiobjective Clustering , 2007, IEEE Transactions on Evolutionary Computation.

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

[3]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[4]  G. De Soete,et al.  Clustering and Classification , 2019, Data-Driven Science and Engineering.

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

[6]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

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

[8]  Jeffrey Horn,et al.  The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems , 2001, EMO.

[9]  Masahiro Tanaka,et al.  GA-based decision support system for multicriteria optimization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[10]  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).

[11]  P. Sopp.  Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[12]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[13]  Robert C. Kohberger,et al.  Cluster Analysis (3rd ed.) , 1994 .

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

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

[16]  T. T. Binh.  MOBES : A multiobjective evolution strategy for constrained optimization problems , 1997 .

[17]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[18]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[19]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[20]  K. C. Seow,et al.  MULTIOBJECTIVE DESIGN OPTIMIZATION BY AN EVOLUTIONARY ALGORITHM , 2001 .