Multitask Particle Swarm Optimization Algorithm Based on Dual Spatial Similarity

Multitask optimization algorithms can simultaneously derive the best solution for different tasks; however, the convergence speed of such algorithms is slow when frequent negative transfers occur. This is primarily because the similarity function of different tasks is designed only in the decision or target space. Moreover, an algorithm is prone to fall into local optima when population diversity is lost. To reduce negative migration and balance diversity and enhance convergence of multitask optimization algorithms, a multitask particle swarm optimization algorithm based on dual spatial similarity (MTPSO-DSS) is developed in this study. A new similarity function is built into the algorithm for the different tasks based on both decision and target spaces, whereby the transfer probability is adaptively adjusted. The new similarity function, which is more rigorous and accurate, can reduce the probability of negative migration and maintain the convergence speed. Furthermore, a new updating method is designed to handle negative migration and increase diversity of search directions. Adaptive mutation and non-allelic gene crossover strategies are designed to increase the diversity of the algorithm and help it escape from local optima. To verify the performance of the proposed algorithm, nine general multitasking optimization test functions are tested via the proposed algorithm, and the results are compared with other eight multitasking algorithms. The proposed algorithm outperformed the other algorithms for most functions in terms of convergence accuracy and speed, and the average improvement in the convergence accuracy compared with the other eight algorithms is between 23.35 and 99.99%.