A boundary search based ACO algorithm coupled with stochastic ranking

In this paper we present a boundary search based ACO algorithm for solving nonlinear constrained optimization problems. The aim of this work is twofold. Firstly, we present a modified search engine which implements a boundary search approach based on a recently proposed ACO metaheuristic for continues problems. Secondly, we propose the incorporation of the stochastic ranking technique to deal with feasible and infeasible solutions during the search which focuses on the boundary region. In our experimental study we compare the overall performance of the proposed ACO algorithm by including two different complementary constraint-handling techniques: a penalty function and stochastic ranking. In addition, we include in our comparison of results the stochastic ranking algorithm, which was originally implemented using an evolution strategy as its search engine.

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

[2]  Nicolas Monmarché,et al.  On how Pachycondyla apicalis ants suggest a new search algorithm , 2000, Future Gener. Comput. Syst..

[3]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[4]  Wang Lei,et al.  Ant system algorithm for optimization in continuous space , 2001, Proceedings of the 2001 IEEE International Conference on Control Applications (CCA'01) (Cat. No.01CH37204).

[5]  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).

[6]  Jie Sheng,et al.  A Method for Solving Optimization Problems in Continuous Space Using Ant Colony Algorithm , 2002, Ant Algorithms.

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

[8]  Panos M. Pardalos,et al.  A Collection of Test Problems for Constrained Global Optimization Algorithms , 1990, Lecture Notes in Computer Science.

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

[10]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[11]  Carlos A. Coello Coello,et al.  Boundary Search for Constrained Numerical Optimization Problems in ACO Algorithms , 2006, ANTS Workshop.

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

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

[14]  Krzysztof Socha,et al.  ACO for Continuous and Mixed-Variable Optimization , 2004, ANTS Workshop.

[15]  M. E. Muller,et al.  A Note on the Generation of Random Normal Deviates , 1958 .

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

[17]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[18]  Martin Middendorf,et al.  A Population Based Approach for ACO , 2002, EvoWorkshops.