Multi-Strategy Improved Teaching–Learning-Based Optimization for Global Optimization and Real-World Engineering Problems

To address the limitations of the traditional Teaching–Learning-Based Optimization (TLBO) algorithm when solving high-dimensional, multimodal, strongly nonlinear, and constrained global optimization problems—such as single search direction, inefficient population information exchange, insufficient local exploitation capability, susceptibility to premature convergence, and low solution accuracy—this paper proposes a multi-strategy collaborative enhanced Teaching–Learning-Based Optimization algorithm (CSTLBO). While retaining the fundamental two-phase framework of the original TLBO, namely the teacher phase and learner phase, three novel strategies are sequentially incorporated: a Collaborative Differential Guidance (CDG) strategy to enrich global search directions, an Elite-Guided Collaborative Interaction (EGCI) strategy to enhance efficient transmission of high-quality population information, and a Quadratic Interpolation Local Refinement (QILR) strategy to improve fine-grained exploitation in promising regions. Together, these strategies enable an adaptive trade-off between broad search capability and refined local optimization. The effectiveness of CSTLBO is systematically assessed using the CEC2017 and CEC2022 benchmark suites, with comparative analyses conducted against multiple advanced algorithms and the baseline TLBO method. Experimental results demonstrate that CSTLBO exhibits significant superiority in terms of convergence speed, solution accuracy, robustness, and statistical performance, particularly in the 100-dimensional CEC2017 benchmark problems and the WSN deployment problem, while maintaining competitive performance on the 10- and 20-dimensional CEC2022 benchmarks. The superiority of CSTLBO is further validated through the Wilcoxon rank-sum test and Friedman mean rank test. Furthermore, the proposed algorithm is applied to the coverage deployment optimization problem in Wireless Sensor Networks (WSNs), a typical high-dimensional engineering problem involving multiple conflicting deployment indicators, which is formulated as a weighted single-objective optimization problem in this study. The results show that CSTLBO achieves a coverage rate of up to 95.71% with a fitness value as low as 0.1344, outperforming the compared algorithms in overall performance. Owing to its simple structure, low computational complexity, and strong generalization capability, CSTLBO provides an efficient and reliable solution for complex global optimization problems and practical engineering applications.