Comparing bio-inspired algorithms in constrained optimization problems

This paper presents a comparison of four bio- inspired algorithms (all seen as search engines) with a similar constraint-handling mechanism (Deb's feasibility rules) to solve constrained optimization problems. The aim is to analyze the performance of traditional versions of each algorithm based on both, final results and on-line behavior. A set of 24 well- known benchmark problems are used in the experiments. Quality and consistency of results per each algorithm are investigated. Furthermore, two performance measures (number of evaluations to reach a feasible solution and progress ratio inside the feasible region) are utilized to compare the on-line behavior of each approach. Based on the obtained results, some conclusions are established.

[1]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[2]  Enrique Raúl Villa Diharce,et al.  PESO+for Constrained Optimization , 2006, IEEE Congress on Evolutionary Computation.

[3]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[4]  Carlos A. Coello Coello,et al.  A constraint-handling mechanism for particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Carlos A. Coello Coello,et al.  Identifying on-line behavior and some sources of difficulty in two competitive approaches for constrained optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[7]  C. Coello,et al.  Cultured differential evolution for constrained optimization , 2006 .

[8]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[9]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[10]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[11]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Janez Brest,et al.  Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[13]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[14]  Rainer Laur,et al.  Constrained Single-Objective Optimization Using Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

[16]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[17]  Aravind Srinivasan,et al.  A Population-Based, Parent Centric Procedure for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[18]  Jouni Lampinen,et al.  Constrained Real-Parameter Optimization with Generalized Differential Evolution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[19]  J. Lampinen.  A constraint handling approach for the differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[20]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[21]  Carlos A. Coello Coello,et al.  Modified Differential Evolution for Constrained Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.