Enhancing the Harris’ Hawk optimiser for single- and multi-objective optimisation
Yit Hong Choo, Zheng Cai, Vu Le, Michael Johnstone, Douglas Creighton, Chee Peng Lim
Abstract
Abstract This paper proposes an enhancement to the Harris’ Hawks Optimisation (HHO) algorithm. Firstly, an enhanced HHO (EHHO) model is developed to solve single-objective optimisation problems (SOPs). EHHO is then further extended to a multi-objective EHHO (MO-EHHO) model to solve multi-objective optimisation problems (MOPs). In EHHO, a nonlinear exploration factor is formulated to replace the original linear exploration method, which improves the exploration capability and facilitate the transition from exploration to exploitation. In addition, the Differential Evolution (DE) scheme is incorporated into EHHO to generate diverse individuals. To replace the DE mutation factor, a chaos strategy that increases randomness to cover wider search areas is adopted. The non-dominated sorting method with the crowding distance is leveraged in MO-EHHO, while a mutation mechanism is employed to increase the diversity of individuals in the external archive for addressing MOPs. Benchmark SOPs and MOPs are used to evaluate EHHO and MO-EHHO models, respectively. The sign test is employed to ascertain the performance of EHHO and MO-EHHO from the statistical perspective. Based on the average ranking method, EHHO and MO-EHHO indicate their efficacy in tackling SOPs and MOPs, as compared with those from the original HHO algorithm, its variants, and many other established evolutionary algorithms.