MESDO: A Multi-Strategy Supply Demand Optimization for Global Optimization and Deployment of Wireless Sensor Network

Bowei Wang, Yuchen Yan, Lingxi Zhu, Shaojie Yin and Yangjian Yang ()
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Bowei Wang: Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA
Yuchen Yan: College of Engineering, City University of Hongkong, Hong Kong 999077, China
Lingxi Zhu: School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M139PL, UK
Shaojie Yin: Taizhou Institute, Zhejiang University, Taizhou 318000, China
Yangjian Yang: Taizhou Institute, Zhejiang University, Taizhou 318000, China

Mathematics, 2025, vol. 13, issue 22, 1-40

Abstract: To address the problems of the traditional Supply–Demand Optimization (SDO) algorithm in wireless sensor network (WSN) node deployment—such as blind search direction, weak global exploration capability, coarse boundary handling, and insufficient maintenance of population diversity—this paper proposes a Multi-Strategy Enhanced Supply–Demand Optimization algorithm (MESDO). The proposed MESDO is validated on the CEC2017 and CEC2022 benchmark test suites. The results demonstrate that MESDO achieves superior performance in unimodal, multimodal, hybrid, and composite function optimization: for unimodal functions, it enhances local exploitation precision via elite-guided search to quickly converge to optimal regions; for multimodal functions, the adaptive differential evolution operator effectively avoids local optima by expanding exploration scope; for hybrid and composite functions, the centroid-based opposition learning boundary control maintains stable population diversity, ensuring adaptability to complex solution spaces. These advantages enable MESDO to effectively avoid premature convergence. According to the Friedman test, MESDO ranks first on CEC2017 (d = 30), CEC2022 (d = 10), and CEC2022 (d = 20), with average rankings of 1.20, 1.67, and 1.33, respectively—significantly outperforming the second-ranked SDO (average rankings of 3.60, 3.25, and 3.83). Finally, MESDO is applied to WSN deployment optimization. Its average coverage rate (86.80%) exceeds that of SDO (84.41%) by 2.39 percentage points, while its minimum coverage (84.80%) is 21.21 percentage points higher than that of AOO (69.96%). Moreover, its standard deviation (8.1308 × 10 −3 ) is the lowest among all compared algorithms. The convergence curve reveals that MESDO achieves 82% coverage within 50 iterations, which is significantly faster than SDO (80 iterations) and IWOA (100 iterations). The node deployment distribution further shows that the generated nodes are uniformly distributed without coverage blind spots. In summary, MESDO demonstrates superior optimization accuracy, convergence speed, and stability in both function optimization and WSN deployment, providing a reliable and efficient approach for WSN deployment optimization.

Keywords: supply demand optimization; wireless sensor network; global optimization; elite-guided search; boundary control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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