A Comparative Review of Fuzzy Reinforced Search Algorithms: Methods and Applications

Abstract

Engineering optimization provides efficient designs that balance performance with resource demand. Metaheuristic algorithms excel at this task, but their lack of adaptability across different problems limits their search capability. In this regard, integrating these methods with auxiliary decision-making mechanisms based on fuzzy logic can considerably improve their search ability. Fuzzy logic empowers these algorithms to adapt their search behavior dynamically based on specific problem characteristics. The current study assesses how this integration improves search efficiency and adaptability to complex and uncertain scenarios, ultimately leading to more effective solutions in engineering optimization. To this end, different fuzzy-reinforced metaheuristic approaches are evaluated, and their search capabilities are compared among themselves and against their standard versions. The selected methods were thoroughly assessed from diverse aspects, including search performance, behavioral process, computational cost, and stability across various problems (e.g., mathematical, mechanical, and structural problems). The acquired results are reported and discussed in detail. Consequently, the attained outcomes indicate that a proper fuzzy-based decision mechanism can considerably improve the search capability of metaheuristic algorithms.