Hybrid Multiobjective Differential Evolution Incorporating Preference Based Local Search

AbstractThe performance of Differential Evolution (DE) for multiobjective optimization problems (MOPs) can be greatly enhanced by hybridizing with other techniques. In this paper, a new hybrid DE incorporating preference based local search is proposed. In every generation, a set of nondominated solutions is generated by DE operation. Usually these solutions distribute unevenly along the obtained nondominated set. To get solutions in the sparse region of the nondominated set, a mini population and preference based local search algorithm is specifically designed, and is used to exploit the sparse region by optimizing an achievement scalarizing function (ASF) with the dynamically adjusted reference point. As a result, multiple solutions in the sparse region can be obtained. Moreover, to retain uniformly spread nondominated solutions, an improved e-dominance strategy, which would not delete the extreme points found during the evolution, is proposed to update the external archive set. Finally, numerical result...

[1]  Andrzej P. Wierzbicki,et al.  The Use of Reference Objectives in Multiobjective Optimization , 1979 .

[2]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[3]  Kaisa Miettinen,et al.  A two-slope achievement scalarizing function for interactive multiobjective optimization , 2012, Comput. Oper. Res..

[4]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[5]  Carlos A. Coello Coello,et al.  An Algorithm Based on Differential Evolution for Multi-Objective Problems , 2005 .

[6]  Carlos A. Coello Coello,et al.  Pareto-adaptive -dominance , 2007, Evolutionary Computation.

[7]  Ning Dong,et al.  A Hybrid Multiobjective Differential Evolution Algorithm Based on Improved e-Dominance , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

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

[9]  K. Miettinen,et al.  Incorporating preference information in interactive reference point methods for multiobjective optimization , 2009 .

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

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

[12]  Yuping Wang,et al.  An orthogonal genetic algorithm with quantization for global numerical optimization , 2001, IEEE Trans. Evol. Comput..

[13]  Francisco Ruiz,et al.  A classification of the weighting schemes in reference point procedures for multiobjective programming , 2009, J. Oper. Res. Soc..

[14]  Kalyanmoy Deb,et al.  Reference point based multi-objective optimization using evolutionary algorithms , 2006, GECCO.

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

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

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

[18]  Andrzej P. Wierzbicki,et al.  A parallel multiple reference point approach for multi-objective optimization , 2010, Eur. J. Oper. Res..

[19]  Wenyin Gong,et al.  An improved multiobjective differential evolution based on Pareto-adaptive epsilon-dominance and orthogonal design , 2009, Eur. J. Oper. Res..

[20]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

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

[22]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[23]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[24]  H. Abbass,et al.  PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[25]  Kalyanmoy Deb,et al.  Evaluating the -Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions , 2005, Evolutionary Computation.

[26]  K..  Multiobjective Optimization Using a Pareto Differential Evolution Approach , 2022 .

[27]  DebK.,et al.  A fast and elitist multiobjective genetic algorithm , 2002 .

[28]  Kalyanmoy Deb,et al.  Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[29]  K. Deb,et al.  Hybrid methods for multi-objective evolutionary algorithms , 2002 .

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

[31]  Rubén Saborido,et al.  A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm , 2015, J. Glob. Optim..

[32]  K..  Multiobjective Optimization Using a Pareto Differential Evolution Approach , 2022 .

[33]  Carlos A. Coello Coello,et al.  g-dominance: Reference point based dominance for multiobjective metaheuristics , 2009, Eur. J. Oper. Res..

[34]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.