Hybrid evolutionary algorithms with heuristic operators for combinatorial optimization problems = 조합 최적화 문제를 위한 휴리스틱 연산자를 이용한 하이브리드 진화 알고리즘

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Recently, there have been much research of robust and powerful optimization methods for solving large and difficult combinatorial problems. As a result, several effective stochastic search algorithms have shown up in the literature, for example, simulated annealing, tabu search, and evolutionary algorithms. Evolutionary algorithms inspired by the natural process of evolution are attracting attentions for dealing with global optimization. The main feature of evolutionary algorithms is the maintenance of a set of solutions that are searched in parallel and the adoption of perturbation mechanism analogous to biological operators. Accordingly, they can be easy parallelizable compared to other methods. There have been a lot of empirical evidences indicating that evolutionary algorithms are good optimization methods, resulting in rapid enlargement of application areas. However, most of the applications have been performed without considering how effective evolutionary algorithms are in solving the problems compared to other search algorithms. This thesis consists of two parts. In the first part, we investigate the features of evolutionary algorithms and examine the efficacy of them by carefully controlled empirical comparisons with simulated annealing. As problem size and ruggedness of the landscape increase, the contribution of crossover to evolutionary search becomes less useful and the evolutionary search becomes less efficient than simulated annealing. The second part of this thesis deals with two possible hybridization techniques to enhance performance of canonical evolutionary algorithms. The first hybrid incorporates independent sophisticated heuristic local search to evolutionary algorithms so as to overcome problems of evolutionary algorithms identified in the first part of this thesis. Experimental results demonstrate that the hybrid is capable of clearly outperforming canonical evolutionary algorithms and is better than simulated annealing for some cla...
Park, Cheol-Hoonresearcher박철훈researcher
한국과학기술원 : 전기및전자공학과,
Issue Date
128040/325007 / 000935139

학위논문(박사) - 한국과학기술원 : 전기및전자공학과, 1997.8, [ xi, 129 p. ]


Stochastic search; Hybrid; Evolutionary algorithm; Simulated annealing; 근사 담금질; 확률적 탐색; 하이브리드; 진화 알고리즘

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