基于CoMOPSO的传感器优化部署方法

A sensor optimization deployment method based on Collaborative evolution Multi-Objective Particle Swarm Optimization

摘要:

传感器优化部署问题是一个涉及传感器覆盖效果、用频冲突概率以及资源利用的多目标优化问题。现有的传感器优化部署方法大多采用加权方式，将多个优化目标转换为单目标进行求解。这种方法不仅依赖先验知识，还会导致最优解的多样性损失。对此，提出一种协同进化多目标粒子群优化(CoMOPSO)算法。设计了一种协同进化框架，通过收敛种群保证高维问题的收敛性，进而快速接近帕累托最优前沿；多样性种群使用??-支配方法保证全局和局部最优解集的完整和多样性；最后采用快速非支配排序和精英个体保留策略提高解的质量。实验结果表明，对于传感器优化部署问题，该方法的反世代距离(IGD)和M3*指标均优于传统优化算法，具有更好的收敛性和多样性，能有效提高传感器网络性能。

Abstract:

The sensor optimization deployment is a multi-objective optimization problem involving sensor coverage effectiveness, frequency conflict probability, and resource utilization. The existing sensor optimization deployment methods mostly adopt weighted approaches to transform multiple optimization objectives into a single objective problem for resolution, which not only relies on prior knowledge but also leads to the loss of diversity in optimal solutions. To address these issues, a Collaborative evolution Multi-Objective Particle Swarm Optimization(CoMOPSO) algorithm is proposed. It designs a collaborative evolution framework that guarantees the convergence of high-dimensional problems through the convergence of the population, and rapidly approaches the Pareto optimal frontier. The diverse population uses the ??-dominance method to ensure the integrity and diversity of the global and local optimal solution sets. A fast non-dominated sorting and elite individual preservation strategy is employed to enhance the quality of solutions. Experimental results demonstrate that, for the sensor optimization deployment problem, the proposed method outperforms traditional optimization algorithms in terms of Inverted Generational Distance(IGD) and M3* indicators, exhibiting better convergence and diversity and effectively improving the performance of sensor networks.