Optimizing Northern Goshawk Algorithm with Fuzzy Logic and Whale Algorithm Strategies

Abstract

Scientists have initiated the examination of living behavioral patterns of organisms, with a primary focus on their quest for sustenance and evasion of predators to ensure their survival. This research endeavors to formulate mathematical models capable of emulating these behaviors, thereby empowering these models to address intricate and demanding mathematical quandaries. In this investigation, two distinct strategies were employed to enhance problem-solving capabilities. The first strategy entailed synergizing the North Goshawk Optimization Algorithm (NGOA) with fuzzy logic (FL). Fuzzy logic was leveraged to impart fuzziness to the initial population and allocate membership grades to all community elements within the confines of the fuzzy logic framework. The second strategy involved the integration of two hybridization approaches: the first through the community and the second via equations between the Fuzzy North Goshawk Optimization Algorithm (NGOA) and the Whale Optimization Algorithm (WOA). The proposed methodology was implemented across ten fundamental functions, revealing a marked superiority of the proposed algorithm when compared to the original version.