Harris's Hawk Multi-Objective Optimizer for Reference Point Problems

摘要

This paper proposes a novel approach called the Harris's Hawk Multi-Objective Optimizer (HHMO), which is used for solving reference point multi-objective problems. This algorithm is based on the grey wolf multi-objective optimization algorithm and motivated by the cooperative hunting behaviors of the Harris's Hawk These hawks are known as the wolf pack of the sky. The hunting party consists of a group of lookout hawks perched above the environment to identify prey and direct a second group of ground hawks towards potential prey, who encircle, flush out and attack the prey. By mathematical modeling these behaviors optimal solution sets around desired reference points for multi-objective problems can be found. HHMO was successful in locating clusters of solutions at or near these desired reference points. This new HHMO algorithm out preformed the previously tested Predator Prey algorithms both in terms of fitness achievement and shorter convergence time.