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Evolutionary Optimization in Dynamic Environments
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Computers and Operations Research
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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Abstract: Many dynamic optimization problems appear in the real world, and to solve them we need to find strategies that can track the optimum as it moves in the search space. In this paper we propose the use of a cooperative metaheuristic to cope with such problems. In this strategy different metaheuristics cooperate under the supervision of a coordinator. This coordinator is able to control the cooperation using a collection of Support Vector Machine models and a fuzzy decision framework. The combination of these two techniques allows us to modify the behavior of the strategy depending on the instance being solved. In order to obtain the models we use a well defined knowledge extraction process, which is performed only once and before operating the strategy. To test the validity of this approach we have applied it to a combinatorial optimization problem and a continuous optimization problem, respectively, Dynamic Knapsack Problem and Moving Peaks Benchmark. The strategy has been compared with other individual and cooperative strategies with very interesting results.