Open AccessDiscrete Fireworks Algorithm for Welding Robot Path Planning
Author(s) -
Xinyu Zhou,
Qingjie Zhao,
Dongxu Zhang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012003
Subject(s) - crossover , motion planning , robot , fireworks , welding , algorithm , computer science , swarm intelligence , robot welding , mathematical optimization , path (computing) , genetic algorithm , particle swarm optimization , artificial intelligence , engineering , mathematics , machine learning , mechanical engineering , chemistry , organic chemistry , programming language
Welding robots are widely used in manufacturing industries, and reasonable welding path planning is an important issue in production efficiency. Welding robot path planning aims to arrange the sequence of weld joints and find the optimal welding path for the robot, which is essentially a combinatorial optimization problem. With the development of artificial intelligence, swarm intelligence algorithms shed new light on this problem. Fireworks algorithm (FWA) is a newly proposed swarm intelligence algorithm, simulating the process of fireworks explosion producing sparks to find the optimal solution. It has shown excellent performance in continuous optimization problems. In this paper, a discrete fireworks algorithm (DFWA), is proposed to solve the welding robot path planning problem. We introduce some operations to the framework of traditional FWA. In DFWA, 2-opt local search and crossover operator are applied on the explosion sparks. A new way of fireworks quality judgment is designed. Mutation operator is implemented for the generation of Gaussian sparks, and the selection strategy is improved. Simulation experiments have been made to verify our method and compare its performance with other current swarm intelligence algorithms. Experimental results show that the proposed method performs well with good convergence, stability and accuracy. It is effective for welding robot path planning.