Teaching–Learning–Studying-Based Optimization with Dance Learning Strategies for Global Optimization Problems and Real-World Applications

This paper addresses two key challenges: low solution accuracy and premature convergence in high-dimensional optimization problems, as well as the difficulty of jointly optimizing coverage, redundancy, and movement cost in wireless sensor network (WSN) deployment. To solve these issues, an improved Teaching–Learning–Studying-Based Optimization algorithm, named TLSBO-DLS, is proposed. Within the original TLSBO framework, three enhancement strategies are incorporated: (1) a dimension-adaptive update probability mechanism to improve fine-grained search capability; (2) a dance learning strategy that enhances dynamic exploration through oscillatory cooperative learning; and (3) an elite adaptive perturbation mechanism based on a Cauchy–Gaussian hybrid distribution to improve convergence accuracy and help escape local optima. Empirical evaluations conducted on the CEC2017, CEC2020, and CEC2022 benchmark datasets indicate that TLSBO-DLS achieves superior performance compared to nine alternative algorithms, exhibiting higher solution precision and faster convergence behavior. Furthermore, its advantage is rigorously confirmed through statistical analyses using the Wilcoxon rank-sum test and the Friedman ranking test. Furthermore, a two-dimensional multi-objective WSN node deployment model is constructed, and TLSBO-DLS is applied to a practical scenario with 30 sensor nodes. The results show that the proposed algorithm achieves a coverage rate of 85.50%, a redundant coverage rate of only 5.15%, and an average node movement distance as low as 15.8471. In terms of global performance, the proposed method surpasses PSO, GWO, WOA, as well as several enhanced TLSBO variants, thereby demonstrating its strong capability and practical value when addressing high-dimensional challenging optimization tasks and real-world engineering problems.