metaheuristic methods; search patterns; second-order systems; evolutionary methods.
DOI:
https://doi.org/10.32870/recibe.v14i2.318Keywords:
metaheuristic methods; search patterns; second-order systems; evolutionary methods.Abstract
Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that exhibit different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this article, a set of new search patterns is presented to efficiently explore the search space. These patterns emulate the response of a second-order system. The proposed set of search patterns has been integrated as a complete search strategy, called the Second Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared on a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. The numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.References
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