Abstract
Human Group Optimization (HGO) algorithm, derived from the previously proposed seeker optimization algorithm (SOA), is a novel swarm intelligence algorithm by simulating human behaviors, especially human searching/foraging behaviors. In this paper, a canonical HGO with local search (L-HGO) is proposed. Based on the benchmark functions provided by CEC2005, the proposed algorithm is compared with several versions of differential evolution (DE) algorithms, particle swarm optimization (PSO) algorithms and covariance matrix adaptation evolution strategy (CMA-ES). The simulation results show that the proposed HGO is competitive or, even, superior to the considered other algorithms for some employed functions.
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Dai, C., Chen, W., Ran, L., Zhang, Y., Du, Y. (2011). Human Group Optimizer with Local Search. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_37
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DOI: https://doi.org/10.1007/978-3-642-21515-5_37
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