A discrete fireworks optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution

Abstract

Purpose

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

Design/methodology/approach

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

Findings

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

Practical implications

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

Originality/value

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Citation

Liu, Z., Nan, Z., Qiu, C., Tan, J., Zhou, J. and Yao, Y. (2019), "A discrete fireworks optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution", Assembly Automation, Vol. 39 No. 2, pp. 323-344. https://doi.org/10.1108/AA-08-2018-0123