An Intelligence Model with Max-Min Strategy for Constrained Evolutionary Optimization

An intelligence model (IM) is proposed for constrained optimization in this paper. In this model, two main issues are considered: first, handling feasible and infeasible individuals in population, and second, recognizing the piecewise continuous Pareto front to avoid unnecessary search, it could reduce the amount of calculation and improve the efficiency of search. In addition, max-min strategy is used in selecting optimization. By integrating IM with evolutionary algorithm (EA), a generic constrained optimization evolutionary (IMEA) is derived. The new algorithm is applied to tackle 7 test instances on the CEC2009 MOEA competition, and the performance is assessed by IGD metric, the results suggest that it outperforms or performs similarly to other algorithms in CEC2009 competition.

[1]  Sai-Ping Li,et al.  A guided Monte Carlo approach to optimization problems , 2003 .

[2]  Yuren Zhou,et al.  An Adaptive Tradeoff Model for Constrained Evolutionary Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[3]  Yuqing Chen,et al.  Multi-Objective Evolutionary Algorithm Based on Dynamical Crossover and Mutation , 2008, 2008 International Conference on Computational Intelligence and Security.

[4]  Gary G. Yen,et al.  Constraint Handling in Multiobjective Evolutionary Optimization , 2009, IEEE Transactions on Evolutionary Computation.

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

[6]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[7]  Hai-Lin,et al.  The multiobjective evolutionary algorithm based on determined weight and sub-regional search , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

[9]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[10]  Yuping Wang,et al.  A Novel Multiobjective Evolutionary Algorithm Based on Min-Max Strategy , 2003, IDEAL.

[11]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[12]  P. Suganthan,et al.  Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods , 2011 .