Multi-strategy and self-adaptive differential sine–cosine algorithm for multi-robot path planning

Abstract

Researchers have conducted studies using various metaheuristic algorithms for multi-robot path planning recently. One of these algorithms, sine–cosine algorithm (SCA) cannot produce satisfactory results in path planning problems, due to a single update strategy. It is necessary to adopt multiple update strategies and improve its performance for a wider set of problems. We have proposed a new multi-strategy self-adaptive differential sine–cosine algorithm (sdSCA) that uses a pool of strategies and allows for more frequent selection of strategies that lead to better solutions. Thus, dependency of SCA on a single strategy has been removed and it has become a more stable for a wider set of problems and convergence of SCA is improved. Firstly, effectiveness of sdSCA was tested in CEC2015 benchmark functions and CEC2020 real-world optimization problems. Performance of sdSCA at these tests is satisfactory. Secondly, sdSCA was applied to online multi-robot path planning in complex environments with static and dynamic obstacles. In this path planning simulation, the proposed algorithm achieved an average improvement of 42% compared to SCA. It also appears to produce results superior to state-of-the-art metaheuristic algorithms.