Fuzzy Logic Controlled Multi-Objective Differential Evolution

In recent years, multi-objective evolutionary algorithms (MOEA) have generated a large research interest. MOEA's attraction stems from their ability to find a set of Pareto solutions rather than any single, aggregated optimal solution for a multi-objective problem. As for single-objective evolutionary algorithms (SOEA), multi-objective evolutionary algorithms also require parameter tuning to achieve desirable performance. In the literature we can find fuzzy logic controllers (FLC's) applied to online parameter control for SOEA. In this paper, we propose to use a FLC to dynamically adjust the parameters of a particular multi-objective differential evolution (MODE) algorithm. The fuzzy logic controlled multi-objective differential evolution (FLC-MODE) is applied to a suite of benchmark functions. Its results are compared to those obtained by using MODE with constant parameter settings. We show that the FLC-MODE obtains better results in 80% of the testing examples. Given that the benchmarks were synthetic test functions, we designed the FLC using only our understanding of the working mechanism of the MODE, without incorporating any additional problem-specific knowledge. When addressing real-world applications, we expect the FLC to be an excellent way for representing and leveraging their associated heuristic knowledge

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

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

[3]  Piero P. Bonissone,et al.  A retrospective view of fuzzy control of evolutionary algorithm resources , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

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

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

[6]  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).

[7]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[8]  Piero P. Bonissone,et al.  Genetic algorithms for automated tuning of fuzzy controllers: a transportation application , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

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

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

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

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

[13]  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.

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

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

[16]  In Schoenauer,et al.  Parallel Problem Solving from Nature , 1990, Lecture Notes in Computer Science.

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

[18]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[19]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

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

[21]  Arthur C. Sanderson,et al.  Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application , 1998, Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell.

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

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