Abstract
This paper describes and demonstrates modifications of the harmony search method to support multimodal structural optimization. Several researchers have recognized the potential of population-based optimization methods, such as genetic algorithms and particle swarm optimization, to support multimodal optimization, that is, generating a range of good alternative solutions, rather than a single best solution. Among these population-based methods is the harmony search method, which has been demonstrated to be efficient and effective in many unimodal structural optimization problems. Toward the goal of making the harmony search method more effective in multimodal optimization, this paper describes a new strategy for generating solutions called close-harmony improvisation, and a new strategy for replacing solutions called local replacement. Examples demonstrate the effect of the two strategies used individually and in tandem. The discussion compares results with conventional harmony search and finds that close-harmony improvisation consistently improves the fitness of the search results, although the effect is sometimes mild, whereas local replacement is quite effective in increasing the diversity of the search result.
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