A Self-Adaptive Topologically Connected-Based Particle Swarm Optimization

Most existing particle swarm optimization (PSO) variants use a single learning strategy and a fixed neighborhood structure for all particles during the search process. The adoption of rigid learning pattern and communication topology may restrict the intelligence level of each particle, hence degrading the performance of PSO in solving the optimization problems with complicated fitness landscapes. Recent studies suggested that the employment of self-adaptive mechanism in adjusting the search strategy and topology connectivity of each particle along the search process may serve as a potential remedy to improve the performance of PSO, especially when dealing with complex problems. For this reason, a self-adaptive topologically connected (SATC)-based PSO equipped with an SATC module and an improved learning framework is proposed. The SATC module is envisioned to facilitate each particle to perform searching with different exploration and exploitation strengths by adaptively modifying its topology connectivity in different searching stages. A modified velocity update scheme and an alternative search operator are also introduced to formulate an improved learning framework to enhance the performance of proposed work further. Substantial numbers of benchmark functions and two real-world optimization problems are used to compare SATC-based PSO (SATCPSO) with several well-established PSO variants. Extensive studies have verified that SATCPSO is more competitive than its peers in most of the tested problems.