School of Computer Science, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2021 Sep 18;21(18):6271. doi: 10.3390/s21186271.
Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes.
最优功率分配（OPA）可以转化为具有约束条件的优化问题，在无线传感器网络（WSNs）中起着关键作用。受蚁群优化启发，本文提出了一种基于多算子的改进约束自适应差分进化（即 IMO-CADE），用于 OPA。所提出的 IMO-CADE 具有以下特点：（i）为了自适应地从不同算子中选择合适的算子，同时考虑算子的反馈和个体的状态来分配选择概率；（ii）采用约束奖励分配来衡量算子的反馈；（iii）参数自适应用于差分进化的参数。为了广泛评估 IMO-CADE 的性能，它用于解决具有不同传感器节点数量的独立和相关观测的 OPA。与其他先进方法相比，仿真结果清楚地表明，IMO-CADE 在整体上表现出最佳性能。因此，IMO-CADE 可以作为 WSN 的 OPA 的有效替代方案，特别是对于具有大量传感器节点的 WSN。
