A Modified Multi-Objective Whale Optimization Algorithm with Dynamic Leader Selection Mechanism

Abstract

The whale optimization algorithm (WOA) has less control parameters and it is easier to implement. The multi-objective whale optimization algorithm (MOWOA) also shows good exploration and exploitation capability. A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism (MMOWOA-DLS) is proposed. First, the opposition-based learning (OBL) is employed to accelerate the convergence speed. Second, a dynamic leader selection mechanism is utilized to improve the solution accuracy. Third, a modified archive grid controller is proposed to delete redundant solutions in external archive. The simulation results show that the performance of MMOWOA-DLS outperforms other algorithms.