Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks

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This paper proposes a parallel fish migration optimization algorithm with compact technology (PCFMO), and designs a sequential communication strategy between groups and a compact technology to save memory space. This paper uses 30 benchmark functions on CEC 2014 and three engineering problems as test benchmarks to compare the PCFMO algorithm with seven well-known algorithms, including Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Harris Hawks Optimization (HHO), Salp Swarm Algorithm (SSA), Fish Migration Optimization (FMO), Archimedes Optimization Algorithm (AOA) and Aquila Optimizer (AO). The experimental results show that the PCFMO algorithm achieves better results and has less space occupied by the population. The comprehensive performance of wireless sensor networks (WSN) is challenged by the battery energy limitation of sensor nodes distributed in specific areas. A proper cluster head set can manage energy consumption reasonably to extend the life cycle of the sensor network and increase the amount of message transmission. This paper takes the energy consumption in each round as the fitness function and adds the memory principle to the PCFMO algorithm to speed up the search for the optimal cluster head set. Compared with the LEACH, HFAPSO and PSO-C algorithms, the PCFMO algorithm based on the memory principle (PCFMO-Memory) can speed up the convergence of finding the optimal cluster head set, extend the life cycle of WSN and increase the amount of message transmission.