An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

Abstract

Tuna swarm optimization algorithm (TSO) is an innovative swarm intelligence algorithm that possesses the advantages of having a small number of adjustable parameters and being straightforward to implement, but the TSO exhibits drawbacks including low computational accuracy and susceptibility to local optima. To solve the shortcomings of TSO, a TSO variant based on behavioral evaluation and simplex strategy is proposed by this study, named SITSO. Firstly, the behavior evaluation mechanism is used to change the updating mechanism of TSO, thereby improving the convergence speed and calculation accuracy of TSO. Secondly, the simplex method enhances the exploitation capability of TSO. Then, simulations of different dimensions of the CEC2017 standard functional test set are performed and compared with a variety of existing mature algorithms to verify the performance of all aspects of the SITSO. Finally, numerous simulation experiments are conducted to address the optimization of wireless sensor network coverage. Based on the experimental results, SITSO outperforms the remaining six comparison algorithms in terms of performance.