Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

中文翻译：

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能量均衡簇头选择方案的混合大象群优化和文化算法，延长无线传感器网络的寿命

无线传感器网络（WSN）中基于聚类的最佳簇头选择被认为是提高网络寿命的有效技术。但是，基于能量稳定，减少延迟和最小化传感器节点之间的距离来强制执行最佳簇头选择，始终是延长WSN中网络寿命的关键挑战。本文提出了一种混合大象放牧优化和文化算法，用于最佳簇头选择（HEHO-CA-OCHS）方案，以延长寿命。该拟议的HEHO-CA-OCHS方案利用了文化算法所构架的信念空间的优点，来定义一个分离算子，该算子对于在搜索空间中构造新的局部最优解很有用。进一步，信念空间的加入有助于在最佳簇头选择下增强搜索功能，从而在最佳开采和勘探过程之间保持平衡。提出的HEHO-CA-OCHS方案通过合并分隔符和氏族更新运算符来有效选择簇头，从而提高了算法的特性，从而延长了网络寿命。据估计，拟议的HEHO-CA-OCHS方案的仿真结果在活动节点的百分比上为11.21％，在死节点的百分比上为13.84％，剩余能量为16.38％，吞吐量为13.94％，网络寿命为19.42 ％与基准簇头选择方案相比。提出的HEHO-CA-OCHS方案通过合并分隔符和氏族更新运算符来有效选择簇头，从而提高了算法的特性，从而延长了网络寿命。据估计，拟议的HEHO-CA-OCHS方案的仿真结果在活动节点的百分比上为11.21％，在死节点的百分比上为13.84％，剩余能量为16.38％，吞吐量为13.94％，网络寿命为19.42 ％与基准簇头选择方案相比。提出的HEHO-CA-OCHS方案通过合并分隔符和氏族更新运算符来有效选择簇头，从而提高了算法的特性，从而延长了网络寿命。据估计，拟议的HEHO-CA-OCHS方案的仿真结果在活动节点的百分比上为11.21％，在死节点的百分比上为13.84％，剩余能量为16.38％，吞吐量为13.94％，网络寿命为19.42 ％与基准簇头选择方案相比。