An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

ABSTRACT

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.

KEYWORDS:

• Adaptive particle swarm optimization
• gradient-based local search
• quasi-Newton method
• inertia weight

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61572241 and 61271385], the National Key R&D Program of China [No. 2017YFC0806600], the Foundation of the Peak of Six Talents of Jiangsu Province [No. 2015-DZXX-024], the Fifth ‘333 High Level Talented Person Cultivating Project’ of Jiangsu Province [No. (2016) III-0845] and the Research Innovation Program for College Graduates of Jiangsu Province [1291170030].