Many-objective African vulture optimization algorithm: A novel approach for many-objective problems

Abstract

Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures’ foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and integrated into the proposed model. In addition, an environmental selection mechanism based on the alternative pool is adapted to improve the selection process to maintain diversity for approximating different parts of the whole Pareto Front (PF). The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) during the population evolution. FAM is based on a convergence measure that promotes convergence and a density measure that promotes variety. Also, a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions. RAS has been designed to help reach out to the missing areas of the PF that the vultures easily miss. Two experiments are conducted to verify and validate the suggested MaAVOA’s performance efficacy. First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. Also, statistical tests are implemented to demonstrate the suggested algorithm’s statistical relevance. Second, MaAVOA has been applied to solve two real-life constrained engineering MaOPs applications, namely, the series-parallel system and overspeed protection for gas turbine problems. The experiments show that the suggested algorithm can tackle many-objective real-world applications and provide promising choices for decision-makers.

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“…Key features of AVOA encompass modeling hunger as solution quality, physical strength as exploration radius, adaptive flight through adjustable parameters, and conflict avoidance via separation distance. We have considered the AVOA as our main candidate algorithm for this study, aiming to maximize visual comfort metrics, for the following reasons: Recent development: AVOA, introduced in 2021 29 , is a newly developed algorithm undergoing active refinement 30 – 35 , offering opportunities for further improvement and application expansion. Documented efficiency: Preliminary studies have demonstrated AVOA's consistently impressive performance, often surpassing established metaheuristic algorithms on benchmark optimization tasks 29 , 35 – 37 .…”

“…Recent development: AVOA, introduced in 2021 29 , is a newly developed algorithm undergoing active refinement 30 – 35 , offering opportunities for further improvement and application expansion.…”