Title: A MultObjective Grey Wolf Optimizer Based on Fuzzy Optimality
Authors: Ahmed Abdulhusseim Jabbar
Volume: 9
Issue: 9
Pages: 5-16
Publication Date: 2025/09/28
Abstract:
This paper explores MultObjective optimization (MOO), a critical methodology for identifying optimal solutions across various domains such as economics, finance, and engineering. We highlight the significance of MOO in enhancing bioeconomic models for fisheries and its application in financial analysis through the niched-Pareto genetic algorithm. Furthermore, the research examines political dynamics via centrality measures in network models and optimizes mechanical engineering designs, specifically shell and tube heat exchangers. Various MOO methods, including the global criterion method, weighted-sum method, ?-constraint method, lexicographic method, and MultObjective evolutionary algorithms (MOEAs) are discussed. Additionally, we present Fuzzy Logic as a framework for managing uncertainty and imprecision, emphasizing its core components, such as membership functions and fuzzy rules. Lastly, we introduce the Grey Wolf Optimization (GWO) algorithm inspired by natural systems, addressing its development and applications. This research aims to create a MultObjective Grey Wolf Optimizer that integrates Fuzzy Optimality, enhancing the effectiveness of solving complex MultObjective optimization challenges.