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The hybridized slime mould and particle swarm optimization algorithms

Literal researches have proved that most of the algorithms are not capable to solve the problems whose solutions are not locating at the Origin. Due to the large ratio for individuals to maintain their historical trajectories in swarms of the slimed mould (SM), the SM algorithms would perform even worse. Therefore, in this paper, the historical best trajectories were introduced to take part in the updating procedure for positions of individuals in swarms. Simulation experiments were carried out and the final results proved that the improved algorithm could increase capabilities of optimization for those non-symmetric problems.