Hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies for numerical optimization problems

Abstract

Due to the simplicity of the learning strategy, the original particle swarm optimization (PSO) has various deficiencies, such as entrapment in local optima, rapid loss of diversity and a poor balance between exploration and exploitation, especially for many complex optimization problems. To overcome these shortcomings, this paper proposes a hybrid particle swarm optimizer with fitness-distance balance and individual self-exploitation strategies, namely, HPSO-FDB-ISE. First, to reduce the probability of becoming trapped in a local optimum for the population, fitness-distance balance is employed to construct an alternative learning exemplar to the global best position. Second, individual self-exploitation is introduced to achieve intelligent exploitation by learning from individual current information for particles. Finally, a nonlinear time-varying inertia weight is used to efficiently balance the exploitation and exploration in the search process. The proposed HPSO-FDB-ISE is evaluated on the CEC 2017 test suite against six state-of-the-art meta-heuristics and seven state-of-the-art PSO variants. Experimental results and statistical analysis reveal that the proposed HPSO-FDB-ISE algorithm yields excellent performances compared to other algorithms that are considered in this paper in the majority of cases.