Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems

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.