Enhanced Whale Optimization Algorithm with Novel Strategies for 3D TSP Problem
Year: 2025
Type: article
Abstract: To address the insufficient global search efficiency of the original Whale Optimization Algorithm (WOA), this paper proposes an enhanced variant (ImWOA) integrating three strategies. First, a dynamic cluster center-guided search mechanism based on K-means clustering divides the population into subgroups that conduct targeted searches around dynamically updated centroids, with real-time centroid recalculation enabling evolutionary adaptation. This strategy innovatively combines global optima with local centroids, significantly improving global exploration while reducing redundant searches. Second, a dual-modal diversity-driven adaptive mutation mechanism simultaneously evaluates spatial distribution and fitness-value diversity to comprehensively characterize population heterogeneity. It dynamically adjusts mutation probability based on diversity states, enhancing robustness. Finally, a pattern search strategy (GPSPositiveBasis2N algorithm) is embedded as a periodic optimization module, synergizing WOA's global exploration with GPSPositiveBasis2N's local precision to boost solution quality and convergence. Evaluated on the CEC2017 benchmark against the original WOA, eight state-of-the-art metaheuristics, and five advanced WOA variants, ImWOA achieves: (1) optimal mean values for 20/29 functions in 30D tests; (2) optimal mean values for 26/29 functions in 100D tests; and (3) first rank in 3D-TSP validation, demonstrating superior capability for complex optimization.
Source: Biomimetics
Institution: North Minzu University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4413413723