Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem. It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization. The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results.

中文翻译：

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无线体域网能量优化技术的性能与评估

创建无线体域网以通过使用人体上的传感器来检索和传输人体健康信息。能源效率被认为是延长网络寿命的首要挑战。处理能效问题的重要机制之一是选择中继节点，可以将其建模为优化问题。如今，受自然启发的算法被广泛用于解决各种优化问题。对此，本文旨在比较最近三种受自然启发的元启发式算法在解决中继节点选择问题方面的性能。已经发现，与粒子群优化相比，使用灰狼优化计算的总能耗降低了 23%，与蚁狮优化相比降低了 16%。结果表明，灰狼优化因其社会等级和狩猎行为而优于其他两种技术。研究结果表明，与众所周知的启发式算法（如粒子群优化和蚁狮优化）相比，灰狼优化能够提供极具竞争力的结果。