Multiple scale self-adaptive cooperation mutation strategy-based particle swarm optimization

Particle Swarm Optimization (PSO) algorithm has lately received great attention due to its powerful search capacity and simplicity in implementation. However, previous studies have demonstrated that PSO still suffers from two key drawbacks of premature convergence and slow convergence, especially when dealing with multi-modal optimization problems. In order to address these two issues, we propose a multiple scale self-adaptive cooperative mutation strategy-based particle swarm optimization algorithm (MSCPSO) in this paper. In the proposed approach, we adopt multi-scale Gaussian mutations with different standard deviations to promote the capacity of sufficiently searching the whole solution space. In the adopted multi-scale mutation strategy, large-scale mutation can make populations explore the global solution space and rapidly locate the better solution area at the early stage, thus avoiding the premature convergence and simultaneously speeding up the convergence, while small-scale mutation can allow the populations to more accurately exploit the local best solution area during the later stage, thus improving the accuracy of final solution. In order to guarantee the convergence speed while avoiding premature convergence, the standard deviations for multi-scale Gaussian mutations would be reduced with the increase of iterations, which can make populations pay more attention to local accurate solution exploitation during the later evolution stage and consequently speed up the convergence. In addition, the threshold for each dimension to execute mutation is also dynamically adjusted according to its previous mutation frequency, which can allow MSCPSO to better balance the global and local search capacities, thus avoiding premature convergence without reducing convergence speed. The extensive experimental results on various benchmark optimization problems demonstrate that the proposed approach is superior to other existing PSO techniques with good robustness. (C) 2020 Elsevier B.V. All rights reserved.