An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization

The Harris Hawks Optimizer (HHO) is a bio-inspired metaheuristic acknowledged for its effectiveness in addressing mono-objective optimization problems. However, its application has been limited to these specific challenges. To overcome this constraint and to navigate complex multi-objective optimization challenges, a Guided Multi-Objective variant of HHO, termed as termed as Guided Multi-Objective Harris Hawks Optimization (GMOHHO), is introduced in this study. In the developed GMOHHO algorithm, an archival mechanism is integrated. This mechanism is specifically designed to store non-dominated solutions and to enhance their retrievability during the search process. Moreover, a robust multi-leader selection procedure is implemented, facilitating the steering of the primary set of solutions towards potential areas within the search space. Further, the Bi-Goal Evolution (BIGE) framework is utilized. This framework aids in the transformation of a search space with multitudinous objectives into a bi-objective one, thereby augmenting environmental selection. This integration ensures a balanced compromise between the convergence and diversity of solutions. The performance of the proposed GMOHHO algorithm was appraised across a series of test functions. The results consistently displayed its supremacy over the conventional HHO approach as well as other cutting-edge multi-objective optimization techniques. With its noteworthy capability to address a broad range of multi-objective optimization problems, the GMOHHO algorithm delivers high-quality solutions within acceptable computational timeframes. This study, therefore, paves the way for a promising approach to multi-objective optimization, potentially expanding the application sphere of the HHO algorithm.