A population-based algorithm, oriented search algorithm (OSA), is proposed to optimize functions in this paper. In OSA, the search-individual imitates human random search behavior, and the search-object simulates an intelligent agent that can transmit oriented information to search-individuals. OSA is tested on thirteen complex benchmark functions. The results are compared with those of particle swarm optimization with inertia weight (PSO-w), particle swarm optimization with constriction factor (PSO-cf) and comprehensive learning particle swarm optimizer (CLPSO). The results show that OSA is superior in convergence efficiency, search precision, convergence property and has the strong ability to escape from the local sub-optima. © 2011 Springer-Verlag.
CITATION STYLE
Zhang, X., & Chen, W. (2011). Oriented search algorithm for function optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6728 LNCS, pp. 338–346). https://doi.org/10.1007/978-3-642-21515-5_40
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