Boundary Search for Constrained Numerical Optimization Problems in ACO Algorithms

This paper presents a novel boundary approach which is included as a constraint-handling technique in an ant colony algorithm. The necessity of approaching the boundary between the feasible and infeasible search space for many constrained optimization problems is a paramount challenge for every constraint-handling technique. Our proposed technique precisely focuses the search on the boundary region and can be either used alone or in combination with other constraint-handling techniques depending on the type and number of problem constraints. For validation purposes, an ant algorithm is adopted as our search engine. We compare our proposed approach with respect to constraint-handling techniques that are representative of the state-of-the-art in constrained evolutionary optimization using a set of standard test functions.

[1]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

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

[3]  Seid H. Pourtakdoust,et al.  An Extension of Ant Colony System to Continuous Optimization Problems , 2004, ANTS Workshop.

[4]  Qin Ling A Method for Solving Optimization Problem in Continuous Space by Using Ant Colony Algorithm , 2002 .

[5]  Andy J. Keane Statistical energy analysis of offshore structures , 1994 .

[6]  Marc Schoenauer,et al.  ASCHEA: new results using adaptive segregational constraint handling , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[8]  Zbigniew Michalewicz,et al.  Evolutionary Computation at the Edge of Feasibility , 1996, PPSN.

[9]  Angus R. Simpson,et al.  A self-adaptive boundary search genetic algorithm and its application to water distribution systems , 2002 .

[10]  Wang Lei,et al.  Further example study on ant system algorithm based continuous space optimization , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[11]  Zbigniew Michalewicz,et al.  A Note on Usefulness of Geometrical Crossover for Numerical Optimization Problems , 1996, Evolutionary Programming.

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