Multi-objective particle swarm optimization algorithm based on collaborative mutation method

Abstract

The invention discloses a multi-objective particle swarm optimization algorithm based on a collaborative mutation method. According to the algorithm, an initial population with the scale of N is randomly generated in a decision space of a to-be-solved problem; in the iteration process, a population is randomly divided into two equal-scale sub-populations P1 and P2. Multi-mode differential variation and non-uniform variation are made on the two sub-populations according to the probability to generate descendant sub-populations P1'and P2'. The descendant sub-populations P1'and P2' are combined to form a temporary intermediate population. Then elite individuals are scrrened from the temporary intermediate population by using a rapid non-dominated sorting method to update an external archive set, and the diversity of the external archive set is maintained by using a crowding distance strategy. A method effectively solves the problems that a multi-objective particle swarm optimization algorithm is prone to premature convergence and unbalanced in search capability.

Description

technical field [0001] The invention relates to the field of intelligent optimization algorithms, and more specifically, to a multi-objective particle swarm optimization algorithm based on a collaborative mutation method. Background technique [0002] With the development of multi-objective evolutionary algorithms, various new heuristic methods and optimization strategies are emerging. These methods and strategies have good performance in solving specific problems, but in the face of complex multi-objective optimization problems, due to these algorithms The global exploration and local mining rely on one method or strategy and often cannot effectively balance the two, which affects the convergence speed of the algorithm and the quality of the solution, and the performance is not satisfactory. In view of this, some researchers try to combine two or more different methods or strategies organically, coordinate the interaction between them, and make the best use of the advantage...

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Problems solved by technology

[0005] The invention provides a multi-objective particle swarm optimization algorithm based on a collaborative mutation method, which can effectively solve the problems that the particle swarm algori

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