Pareto-based multi-objective differential evolution

Evolutionary multiobjective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multiobjective problem. The purpose is to describe a newly developed evolutionary approach-Pareto-based multiobjective differential evolution (MODE). The concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multiobjective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.

[1]  Prabhat Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

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

[3]  Arthur C. Sanderson,et al.  Multisensor Fusion - A Minimal Representation Framework , 1999, Series in Intelligent Control and Intelligent Automation.

[4]  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).

[5]  P. Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[6]  Peter J. Fleming,et al.  On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers , 1996, PPSN.

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

[8]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

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

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

[11]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

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

[13]  Arthur C. Sanderson,et al.  Minimal representation multisensor fusion using differential evolution , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[14]  C. A. Coello Coello,et al.  A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques , 1999, Knowledge and Information Systems.

[15]  Kalyanmoy Deb,et al.  Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence , 2001, EMO.

[16]  Michael P. Fourman,et al.  Compaction of Symbolic Layout Using Genetic Algorithms , 1985, ICGA.

[17]  J. David Schaffer,et al.  Multi-Objective Learning via Genetic Algorithms , 1985, IJCAI.

[18]  Arthur C. Sanderson,et al.  Multi-objective differential evolution and its application to enterprise planning , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

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

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

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

[22]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

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