Enhanced Multi-Object Dwarf Mongoose Algorithm for Optimization Stochastic Data Fusion Wireless Sensor Network Deployment - Li, Shumin; Zhou, Yongquan; Luo, Qifang (Computer modeling in engineering & sciences, 2025)
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research. However, the current research on wireless sensor network deployment problems uses overly simplistic models, and there is a significant gap between the research results and actual wireless sensor networks. Some scholars have now modeled data fusion networks to make them more suitable for practical applications. This paper will explore the deployment problem of a stochastic data fusion wireless sensor network (SDFWSN), a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection. The deployment problem of SDFWSN is modeled as a multi-objective optimization problem. The network life cycle, spatiotemporal coverage, detection rate, and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes. This paper proposes an enhanced multi-objective mongoose optimization algorithm (EMODMOA) to solve the deployment problem of SDFWSN. First, to overcome the shortcomings of the DMOA algorithm, such as its low convergence and tendency to get stuck in a local optimum, an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm. The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor (KNN) algorithm. To verify the effectiveness of the proposed algorithm, the EMODMOA algorithm was tested at CEC 2020 and achieved good results. In the SDFWSN deployment problem, the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II (NSGAII), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and Multi-Objective Grey Wolf Optimizer (MOGWO). By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms, the algorithm outperforms the other algorithms in the SDFWSN deployment results. To better demonstrate the superiority of the algorithm, simulations of diverse test cases were also performed, and good results were obtained.