- Title
- Search space reduction technique for constrained optimization with tiny feasible space
- Creator
- Ullah, Abu S. S. M. Barkat; Sarker, Ruhul; Cornforth, David
- Publisher Link
- http://dx.doi.org/10th Annual Genetic and Evolutionary Computation Conference. GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (Atlanta, GA 12-16 July, 2008) p. 881-888
- Publisher Link
- http://dx.doi.org/10.1145/1389095.1389268
- Publisher
- Association for Computing Machinery (ACM)
- Resource Type
- conference paper
- Date
- 2008
- Description
- 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.
- Subject
- evolutionary algorithms; evolutionary agent systems; genetic algorithms; agent-based systems; nonlinear programming; constrained optimization; search space reduction
- Identifier
- http://hdl.handle.net/1959.13/1057720
- Identifier
- uon:16246
- Identifier
- ISBN:9781605581309
- Language
- eng
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