Swarm Robots Search for Multiple Targets Based on Historical Optimal Weighting Grey Wolf Optimization

This study investigates the problem of swarm robots searching for multiple targets in an unknown environment. We propose Historical Optimal Weighting Grey Wolf Optimization (HOWGWO) algorithm based on improved grouping strategy. In HOWGWO algorithm, we gather and update every individual grey wolf’s historical optimal position rank wolves merit their position. The prey is dynamically estimated by leader wolf, all move towards prey’s To solve multi-target search, integrate with strategy divide into two stages: random walk stage dynamic stage. During stage, randomly positions. generates search auxiliary points (SAPs) adopting wolves’ These SAPs are then utilized to different prey. re-generated using optimum positions single wolf after have been updated, rather than just those belonging a specific group. effectiveness proposed extensively assessed 30 dimensions CEC 2017 test suite, which simulates unimodal, multimodal, hybrid, composition problems. Then, obtained results compared competitors, including GWO, PSO EGWO, statistically analyzed through Friedman’s test. Ultimately, simulations performed simulate real experimental statistical analysis confirm that has fast convergence speed solution quality solving global optimization problems