Human group optimiser for global numerical optimisation

Autor:	Lei Ma, Ouyang Jiajun, Weirong Chen, Chaohua Dai, Yunfang Zhu
Rok vydání:	2014
Předmět:	Mathematical optimization education.field_of_study Fuzzy rule General Computer Science Computer science Population Particle swarm optimization Swarm intelligence Theoretical Computer Science Search algorithm Differential evolution CMA-ES Evolution strategy education
Zdroj:	International Journal of Bio-Inspired Computation. 6:333
ISSN:	1758-0374 1758-0366
DOI:	10.1504/ijbic.2014.065586
Popis:	In this paper, the previously proposed seeker optimisation algorithm (SOA) is renamed as human group optimisation (HGO) algorithm, which is a novel population-based heuristic stochastic search algorithm by simulating human group searching behaviours. In this algorithm, the choice of search direction is based on empirical gradients by evaluating the responses to the position changes, and the decision of step length is based on human-unique uncertainty reasoning by using a simple fuzzy rule. Furthermore, a canonical version of HGO is proposed. Based on the benchmark functions provided by CEC2005, the canonical HGO is compared with differential evolution (DE) algorithms, particle swarm optimisation (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 listed other algorithms for some employed functions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::911d3bfa38d7afa4626549622e28f5b4 https://doi.org/10.1504/ijbic.2014.065586 Zobrazit plný text záznamu