Swarm-Intelligence Optimization Method for Dynamic Optimization Problem

Performance of a Novel Enhanced Sparrow Search Algorithm for Engineering Design Process: Coverage Optimization in Wireless Sensor Network

Burgeoning swarm intelligence techniques have been creating a feasible theoretical computational method for the modeling, simulation, and optimization of complex systems. This study aims to increase the coverage of a wireless sensor network (WSN) and puts forward an enhanced version of the sparrow search algorithm (SSA) as a processing tool to achieve this optimization. The enhancement of the algorithm covers three aspects. Firstly, the Latin hypercube sampling technique is utilized to generate the initial population to obtain a more uniform distribution in the search space. Secondly, a sine cosine algorithm with adaptive adjustment and the Lévy flight strategy are introduced as new optimization equations to enhance the convergence efficiency of the algorithm. Finally, to optimize the individuals with poor fitness in the population, a novel mutation disturbance mechanism is introduced at the end of each iteration. Through numerical tests of 13 benchmark functions, the experimental results show that the proposed enhanced algorithm can converge to the optimum faster and has a more stable average value, reflecting its advantages in convergence speed, robustness, and anti-local extremum ability. For the WSN coverage problem, this paper established a current optimization framework based on the swarm intelligence algorithms, and further investigated the performance of nine algorithms applied to the process. The simulation results indicate that the proposed method achieves the highest coverage rate of 97.66% (on average) among the nine algorithms in the calculation cases, which is increased by 13.00% compared with the original sparrow search algorithm and outperforms other methods by 1.47% to 15.34%.