A Multistrategy Enhanced Starfish Optimization Algorithm for Wireless Sensor Network Deployment

Wireless sensor network (WSN) deployment optimization is crucial for IoT sensing, requiring high coverage with limited nodes in complex search spaces. This paper proposes a multistrategy enhanced starfish optimization algorithm (MSESFOA) to address weaknesses of SFOA. MSESFOA introduces adaptive exploration regulation, a stage-wise combined mutation, and multiarm cooperative recombination, improving exploration flexibility, diversity preservation, and information fusion. Experiments on CEC 2017 and CEC 2022 show competitive accuracy, stability, and low deviation over 30 runs. In the tested 2D and 3D WSN cases, MSESFOA achieves lower mean uncovered ratios than SFOA, while the displayed deployment maps generally show fewer coverage gaps. The results support the competitiveness of MSESFOA for coverage optimization across the tested 2D and 3D scenarios with homogeneous sensors, a fixed sensor count, and a fixed sensing radius.