Title: Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems
Authors: Zhengxuan Jia; Haibin Duan; Yuhui Shi
Addresses: State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (BUAA), Beijing 100191, China ' State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (BUAA), Beijing 100191, China ' Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
Abstract: Inspired by the brainstorming process of human beings, the brain storm optimisation algorithm, a new swarm intelligence algorithm, is proposed and has been applied in many fields in recent years. In this paper, a novel bio-inspired computation algorithm based on the brain storm optimisation algorithm and simulated annealing approach is proposed to solve continuous optimisation problems. The proposed algorithm integrates the simulated annealing process into the brain storm optimisation algorithm. The integrated part is in charge of creation of new individuals in later stages of evolution process, replacing the creation operator. The proposed algorithm is applied to solve 13 benchmark unconstrained continuous optimisation problems, and is compared with three state-of-the-art evolutionary algorithms: particle swarm optimisation, differential evolution, and brain storm optimisation algorithm. Experimental results show that the proposed algorithm produced a significant improvement over the brain storm optimisation algorithm and generally out performed the other three in terms of mean value, standard deviation, best fitness value ever found and convergence speed which can be seen from the evolution curve.
Keywords: brain storm optimisation; BSO; bio-inspired computation; simulated annealing; evolutionary computation; continuous optimisation; brainstorming; swarm intelligence; particle swarm optimisation; PSO; differential evolution.
International Journal of Bio-Inspired Computation, 2016 Vol.8 No.2, pp.109 - 121
Available online: 04 May 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article