Multiobjective extremal optimization with applications to engineering design

In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization (MOEO), to solve the constrained multiobjective optimization problems (MOPs). The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-II, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.

[1]  Tim Hendtlass,et al.  On the Behaviour of Extremal Optimisation When Solving Problems with Hidden Dynamics , 2006, IEA/AIE.

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

[3]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[4]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[5]  Min-Rong Chen,et al.  Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization , 2006, 2006 International Conference on Computational Intelligence and Security.

[6]  Min-Rong Chen,et al.  Studies on Extremal Optimization and Its Applications in Solving RealWorld Optimization Problems , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[7]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[8]  Min-Rong Chen,et al.  A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization , 2008, Eur. J. Oper. Res..

[9]  C. A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Computational Intelligence Magazine.

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

[11]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[12]  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).

[13]  Tim Hendtlass,et al.  Solving Problems with Hidden Dynamics – Comparison of Extremal Optimisation and Ant Colony System , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  Tang,et al.  Self-organized criticality. , 1988, Physical review. A, General physics.

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

[16]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[17]  Alan D. Christiansen,et al.  An empirical study of evolutionary techniques for multiobjective optimization in engineering design , 1996 .

[18]  Dirk Thierens,et al.  The balance between proximity and diversity in multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

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

[20]  A. Baykasoğlu.  Applying multiple objective tabu search to continuous optimization problems with a simple neighbourhood strategy , 2006 .

[21]  A. Percus,et al.  Nature's Way of Optimizing , 1999, Artif. Intell..