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