Adaptive brainstorm optimisation with multiple strategies

Abstract

Brainstorm optimisation (BSO) algorithm is a recently developed swarm intelligence algorithm inspired by the human problem-solving process. BSO has been shown to be an efficient method for creating better ideas to deal with complex problems. The original BSO suffers from low convergence and is easily trapped in local optima due to the improper balance between global exploration and local exploitation. Motivated by the memetic framework, an adaptive BSO with two complementary strategies (AMBSO) is proposed in this study. In AMBSO, a differential-based mutation technique is designed for global exploration improvement and a sub-gradient strategy is integrated for local exploitation enhancement. To dynamically trigger the appropriate strategy, an adaptive selection mechanism based on historical effectiveness is developed. The proposed algorithm is tested on 30 benchmark functions with various properties, such as unimodal, multimodal, shifted and rotated problems, in dimensions of 10, 30 and 50 to verify their scalable performance. Six state-of-the-art optimisation algorithms are included for comparison. Experimental results indicate the effectiveness of AMBSO in terms of solution quality and convergence speed.

Appendix

Properties of benchmark functions (‘MM’ denotes ‘multimodal’, ‘Se’ denotes ‘separable’, ‘Sf’ denotes ‘shifted’, ‘Rt’ denotes ‘rotated’, ‘Ns’ denotes ‘noisy’, and ‘Ms’ denotes ‘Mis-scaled’, The value of the corresponding column is ‘Y’ if the function has the specific property, otherwise, it is “N”) (Table 14).