Bifurcated particle swarm optimizer with topology learning particles

The flying speed and trajectory of particles are subject to several factors, including neighborhood structures, inertia weight, and acceleration coefficients. This paper improves particle swarm optimizer by exploiting these factors. Specifically, it proposes an approach to adjust the neighborhood structures of particles adaptively. The search task is divided between two groups of particles, termed even and uneven, to perform a vigorous in-depth search. Each particle group pursues a different objective and conducts its search in a different manner. Even particles adaptively adjust their number of attractors and neighborhood radiuses to experience various flying trajectories and paces. Each uneven particle follows a single even one for a while until it is assigned to another even particle. Uneven particles are responsible for performing fine-grained searches in the vicinity of their associated even particles as well as their previously experienced locations. A tree structure is utilized to implement the neighborhood structures of the proposed method. In the presented structure, particles can experience large neighborhoods by choosing their attractors from higher levels of the tree. The proposed method is experimentally investigated on the comprehensive CEC2013 benchmark set and two challenging real-world problems: non-uniform circular antenna array synthesis and image segmentation. The comparison results with advanced particle swarm optimization algorithms demonstrate that search bifurcation and topology adjustment can significantly improve particle swarm optimization. Experimental results also indicate that the proposed method can be successfully employed for solving challenging real-world problems with various characteristics. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper improves particle swarm optimizer by adjusting the neighborhood structures of particles, dividing the search task between even and uneven particles for deep search, and utilizing a tree structure for neighborhood implementation. Experimental results show significant improvement in particle swarm optimization and successful application in solving challenging real-world problems.