TL;DR
TAM_RBMO is proposed, a scalable, trend-aware mechanism-driven metaheuristic that explicitly encodes and exploits search-history information to improve directed exploration and reliable convergence in complex, multimodal optimization problems.
This paper develops a hybrid crossover-based optimization framework that enhances population interaction and improves search efficiency, and is applied to UAV path planning, formulated as a constrained optimization problem, demonstrating its effectiveness and scalability in complex engineering scenarios.
Path planning is essential to autonomous navigation in mobile robots, and its performance directly affects task execution efficiency and operational safety. To address the limited convergence accuracy, susceptibility to local optima, and limited late-stage exploitation capability of the walrus optimizer (WO) in path pl...
By prioritizing absolute search stability and robustness in high-dimensional tasks, the proposed algorithm attains an optimal balance between convergence quality and practical engineering efficiency, proving exceptionally effective in complex path-planning scenarios.
X-DTC-GL is introduced, a novel DIRECT-type algorithm that incorporates dynamic partitioning and hybridization techniques that improve performance within the partition-based framework, strengthening the algorithm's competitiveness in state-of-the-art black-box optimization.
The proposed SPO provides an effective alternative optimization tool for complex constrained engineering optimization tasks such as 3D UAV path planning and significantly outperforms 14 mainstream metaheuristic algorithms, including PSO, DE, SHADE, and DBO, on most test functions.
An enhanced sand cat swarm algorithm (ESC-SCSA) is developed and comprehensively evaluated through sensitivity analysis, ablation studies, convergence analysis, and statistical significance tests, demonstrating its effectiveness and potential generalization capability across both continuous and discrete optimization pr...
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