Strength Pareto Multigroup Search Optimizer for Multiobjective Optimal Reactive Power Dispatch

Abstract

Multiobjective optimal reactive power dispatch (MORD) is a highly constrained nonlinear nondifferential multimodal Pareto optimization problem with a mixture of continuous and discrete variables. This paper presents a novel strength Pareto multigroup search optimizer (SPMGSO) for solving this challenging problem. In the proposed algorithm, a number of enhancement mechanisms, such as ring-migration synergistic cooperation, crowding entropy constraint treatment, and chaotic logistic dispersion, were developed to effectively strengthen the diversity and distribution of the resulting nondominated front (NDF). The final best compromise solution is then extracted from the NDF using a hierarchical clustering and an equilibria-based multicriteria decision-making scheme. In addition, the multicore processing was introduced to parallelize the multigroup search so as to greatly improve the execution speed of the algorithm. Computational studies on the benchmark IEEE 30-bus and 162-bus power systems have confirmed the superior performance and improvement of the proposed algorithm for solving this Pareto problem, and demonstrated its applicability and capability to cope with the high-dimensional MORD problems with multiple operational objectives.