Work

Multi-objective Optimization Based on Improved Differential Evolution Algorithm

Year: 2014

Type: article

Abstract: On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions.

Source: TELKOMNIKA (Telecommunication Computing Electronics and Control)

DOI: 10.12928/telkomnika.v12i4.531

Authors: Shuqiang Wang, Jianli Ma

Institution: Hebei University of Engineering

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W1900971124

FWCI: 0.49

Cites: 14

Cited by: 2

Related to: 10