Search Space Filling and Shrinking Based to Solve Constraint Optimization Problems

• Published in: Advances in Intelligent Computing pp. 986 - 994
• Main Authors: Hong, Yi, Ren, Qingsheng, Zeng, Jin, Zhang, Ying
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Combinatorial problem; Optimization method; Combinatorial optimization; Penalty method; Search algorithm; Constrained optimization; Penalty function; Combinatorial method; Genetic algorithm; Filling; Repair; Artificial intelligence; Mathematical programming
• ISBN: 3540282262; 9783540282266
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11538059_102

Genetic algorithm (GA) is an effective method to tackle combinatorial optimization problems. Since the limitation of encoding method, the search space of GA should be regular. Unfortunately, for constraint optimizations, this precondition is unsatisfied. To obtain a regular search space, a commonly used method is penalty functions. But the setting of a good penalty function is difficult. In this paper, a novel algorithm, called search space filling and shrinking algorithm (SSFSA), is proposed. SSFSA first seeks a smaller search space which covers all the feasible domains, then fills the unfeasible search space to acquire a regular search space. Search space shrinking diminishes the search space, so shortens the searching time. Search space filling repairs the irregular search space, and makes GA execute effectively. Experimental results show that SSFSA outperforms penalty methods’.