Year: 2019
Type: conference-paper
Abstract: Multi-objective optimization (MOP) is a hot and difficult problem in the field of industrial production and scientific research, involving simultaneous optimization of multiple objectives. In this paper, an improved decomposition-based multi-objective evolutionary algorithm is proposed to solve MOP. Firstly, the Latin hypercube sampling method is used to generate the initial population in order to improve the diversity of the initial population and the probability of convergence to the global optimal solution. Secondly, the adaptive differential crossover operator is used to adjust the parameter settings adaptively for different problems, including the differential proportionality factor and crossover probability, so as to improve the performance of the algorithm. Finally, the reference point is set to coordinate origin, which can pull the population back to the real front faster and accelerate the convergence speed of the population. In a group of benchmark problems, ZDT and DTLZ test problems, the algorithm is widely compared with four most advanced algorithms. The experimental results show that the algorithm has better ability to solve MOP problems.
Authors: Jiaxin Han, Manman He, Ying Cuan
Institution: Xi'an Shiyou University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2986286314