Particle swarm optimization with increasing topology connectivity

In this paper, we propose a new variant of particle swarm optimization (PSO), namely PSO with increasing topology connectivity (PSO-ITC), to solve unconstrained single-objective optimization problems with continuous search space. Specifically, an ITC module is developed to achieve better control of exploration/exploitation searches by linearly increasing the particleʹs topology connectivity with time as well as performing the shuffling mechanism. Furthermore, we introduce a new learning framework that consists of a new velocity update mechanism and a new neighborhood search operator that aims to enhance the algorithmʹs searching performance. The proposed PSO-ITC is extensively evaluated across 20 benchmark functions with various features as well as two engineering design problems. Simulation results reveal that the performance of the PSO-ITC is superior to nine other PSO variants and six cutting-edge metaheuristic search algorithms.