Search space reduction technique for constrained optimization with tiny feasible space

The hurdles in solving Constrained Optimization Problems (COP) arise from the challenge of searching a huge variable space in order to locate feasible points with acceptable solution quality. It becomes even more challenging when the feasible space is very tiny compare to the search space. Usually, the quality of the initial solutions influences the performance of the algorithm in solving such problems. In this paper, we discuss an Evolutionary Agent System (EAS) for solving COPs. In EAS, we treat each individual in the population as an agent. To enhance the performance of EAS for solving COPs with tiny feasible space, we propose a Search Space Reduction Technique (SSRT) as an initial step of our algorithm. SSRT directs the selected infeasible agents in the initial population to move towards the feasible space. The performance of the proposed algorithm is tested on a number of test problems and a real world case problem. The experimental results show that SSRT not only improves the solution quality but also speed up the processing time of the algorithm.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[3]  Zbigniew Michalewicz,et al.  Genetic Algorithms Plus Data Structures Equals Evolution Programs , 1994 .

[4]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[5]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

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

[7]  Zbigniew Michalewicz,et al.  GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints , 1996, CACM.

[8]  R. Sarker,et al.  Determination of optimum crop mix for crop cultivation in Bangladesh , 1997 .

[9]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization , 1999, Evolutionary Computation.

[10]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

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

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

[13]  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 .

[14]  Ruhul A. Sarker,et al.  Modelling a nationwide crop planning problem using a multiple criteria decision making tool , 2002 .

[15]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[16]  Jonathan A. Wright,et al.  Self-adaptive fitness formulation for constrained optimization , 2003, IEEE Trans. Evol. Comput..

[17]  Jing Liu,et al.  A multiagent genetic algorithm for global numerical optimization , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[19]  J. V. Rauff,et al.  Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence , 2005 .

[20]  Tapabrata Ray,et al.  Multiobjective Evolutionary Algorithms for solving Constrained Optimization Problems , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[21]  Jing Liu,et al.  A multiagent evolutionary algorithm for constraint satisfaction problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  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.

[23]  Anthony Chen,et al.  Constraint handling in genetic algorithms using a gradient-based repair method , 2006, Comput. Oper. Res..

[24]  Marek Kisiel-Dorohinicki,et al.  Semi-elitist Evolutionary Multi-agent System for Multiobjective Optimization , 2006, International Conference on Computational Science.

[25]  Marek Kisiel-Dorohinicki,et al.  Maintaining Diversity in Agent-Based Evolutionary Computation , 2006, International Conference on Computational Science.

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

[27]  Ruhul A. Sarker,et al.  A Combined MA-GA Approach for Solving Constrained Optimization Problems , 2007, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007).

[28]  Ruhul A. Sarker,et al.  An Evolutionary Agent System for Mathematical Programming , 2007, ISICA.

[29]  Ruhul A. Sarker,et al.  An agent-based memetic algorithm (AMA) for solving constrained optimazation problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[30]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[31]  P. Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real- Parameter Optimization , 2010 .