Majlesi Journal of Electrical Engineering Improving lifetime of WSN using modified Moth Flame Optimization Volume 17 (2023) Issue 3, September 2023 2024 02 03 Improving lifetime of WSN using modified Moth Flame Optimization 10.30486/mjee.2023.1978992.1062 EN Methaq Mahdi Ali Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran Razieh Asgarnezhad Department of Computer Engineering, Aghigh Institute of Higher Education Shahinshahr, 8314678755, Isfahan, Iran Journal Article 2024 02 03 The focus is on the longevity and energy efficiency of wireless sensor networks (WSN). WSNs face many obstacles in terms of data transmission. WSNs face difficulties in reducing energy output and shortening life cycles, including node configuration, leader selection, and optimal routing selection. The provisioning of nodes, selection of cluster leaders, and optimal paths have all been recommended using many current methods. However, none of the currently used methods yield sufficient grid energy optimization results. Therefore, this study proposes a modified Moth Flame Optimization Algorithm (MFOOA). Nature passed it on to us. The main inspiration for this optimizer is the lateral flight pattern used by moths in nature. At night, the moth maintains a constant angle to the moon. This is a particularly efficient way to drive long distances in a straight line. Nevertheless, artificial light is everywhere around these amazing creatures, encircling them in a fruitless and deadly spiral. Here, this behavior is theoretically modeled for optimization. The suggested program places the sensor nodes using the flame optimization technique. These sensor nodes might be either dynamic or static depending on the network scenario. The cluster head and the optimum route are chosen using this technique. Within the predetermined search space, it also does phase balancing between the exploration and development phases. In terms of residual energy, sensor node lifetime, used energy, end-to-end latency, and a maximum number of cycles, it differs from current classical and swarm intelligence (SI) techniques. According to the results, MFOOA is superior to its counterpart.