Assembly sequence planning method based on improved particle swarm algorithm

Abstract

The invention discloses an assembly sequence planning method based on an improved particle swarm algorithm. The method gives consideration to the problems that in an assembly problem solving process,the particle swarm algorithm is liable to fall into local optimization, multiple optimized solutions appear, but the efficiency of the practical feasible optimal solution is low, so the convergence precision and the global search capability of the algorithm are improved by adaptively adjusting the w parameter on the basis of improving the w parameter in the standard particle swarm algorithm according to the practical updating condition of the global optimal value in the iteration process and judging whether the global optimal value is changed for two times before and after. A w fixed mode of atraditional standard is improved into a non-continuously changing adjustable parameter mode taking the iteration frequency as the variable under the condition of considering the change of a globallyoptimal value, thereby effectively improving the convergence accuracy and the global search capability of the particle swarm algorithm, and solving the problem of low feasible solution efficiency in the assembly sequence planning problem.

Description

technical field [0001] The invention relates to an assembly sequence planning method, which is based on the optimization principle of a standard particle swarm algorithm, and is applicable to multi-objective optimization problems such as assembly sequence planning and the like by redefining and improving the original particle swarm algorithm. Background technique [0002] Assembly sequence planning seriously affects the actual efficiency and economic cost of product production. It is one of the important links in the digital assembly process. At present, scholars at home and abroad have conducted a lot of research on it. With the wide application of swarm intelligence, genetic algorithms, frogs Jumping algorithm, ant colony algorithm, memetic algorithm, particle swarm algorithm, etc. have all been successfully applied in this field. However, since the optimization of assembly problems is a discrete problem, a lot of algorithm debugging work is required in the early stage of s...

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Problems solved by technology

[0002] Assembly sequence planning seriously affects the actual efficiency and economic cost of product production. It is one of the important links in the digital assembly process. At present, scholars at home and abroad have conducted a lot of research on it. With the wide application of swarm intelligence, genetic algorithms, frogs Jumping algorithm, ant colony algorithm, memetic algorithm, particle swarm algorithm, etc. have all been successfully applied in this field. However, since the optimization of assembly problems is a discrete problem, a lot of algorithm debugging work is required in the early stage of solving, such as the selection of genetic algorithms. factor, cross factor, variation factor, operation index, etc., pheromone parameters in the ant colony algorithm, etc. Therefore, with the help of the advantages of the particle swarm algorithm, consider improving the particle swarm algorithm to solve the assembly sequence planning problem

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