The coverage optimization of wireless visual sensor networks (WVSNs) with changeable field of views (FOVs) brings more challenges on the decision making of modern cyber–physical​ systems as it is a multi-objective decision problem where some contradictory indices, e.g. the sensor coverage and the redundancy, should be taken into consideration concurrently. In this paper, a novel multi-species evolutionary algorithm (MSEA) is proposed to address this issue by introducing a multi-species evolution scheme to enhance the search ability. A competition mechanism based on the deductive sort and the crowding distances is developed to facilitate the generation of Pareto front and the elitist individuals are evolved from a multi-species hybrid population. Comparative results show a better balancing performance between the exploration and the exploitation of the proposed algorithm which induces a strong approximation to the feasible WVSN managements.

具有可变视场（FOV）的无线视觉传感器网络（WVSN）的覆盖范围优化对现代网络物理系统的决策提出了更多挑战，因为它是一个多目标决策问题，其中一些矛盾的指标（例如传感器）覆盖范围和冗余度应同时考虑。本文提出了一种新颖的多物种进化算法（MSEA），通过引入一种多物种进化方案来增强搜索能力。建立了一种基于演绎类别和拥挤距离的竞争机制，以促进帕累托阵线的产生，而精英分子则是从多物种混合种群中进化而来的。