An enhanced multi-strategy exponential distribution optimizer and its application in wireless sensor network coverage optimization

ZP Qiu and ZT Lu and YX Wen, CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 29, 64 (2025).

DOI: 10.1007/s10586-025-05811-1

Exponential Distribution Optimizer (EDO) is a mathematics-based meta- heuristic algorithm with certain advantages. However, it suffers from some shortcomings, such as the inability to balance exploitation and exploration when facing complex optimization problems, as well as the difficulty of maintaining population diversity in the later optimization phases. To address these shortcomings, this paper proposes a Multi- strategy Enhanced Exponential Distribution Optimizer (EMEDO) that contains three enhancement strategies, aiming to strengthen the search performance of the basic EDO. A guided learning strategy is employed to achieve a balance between exploitation and exploration. Two covariance- based guiding strategies are proposed to enhance the exploitation and exploration capabilities, respectively. Finally, a dimensional non- monopoly search strategy is proposed to further improve the quality of the optimal individual. Twenty-nine CEC2017 benchmark test functions and wireless sensor coverage optimization problems are applied to examine the performance of EMEDO and compare it with highly cited, recently proposed meta-heuristic algorithms. Experimental results demonstrate that the proposed EMEDO exhibits a significant advantage in convergence performance and is able to consistently deliver high-quality solutions. Across dimensions of 10D, 30D, 50D, and 100D, EMEDO achieved average ranks of 2.552, 2.276, 2.931, and 2.034, respectively, outperforming the comparison algorithms on more than half of the benchmark functions at each dimensionality. In summary, EMEDO represents a promising variant among meta-heuristic algorithms.

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