Work

The improved equilibrium optimization algorithm with multiple updating discipline

Year: 2020

Type: conference-paper

Abstract: In this paper, a hybridization algorithm of the equilibrium optimization (EO) algorithm and the slime mould (SM) algorithm was proposed. The better performance of the SM algorithm in optimization both benchmark functions and real engineering problems were all encouraging. And consequently, the multiple updating discipline for individuals in swarms was introduced to the EO algorithm. Simulation experiments were carried out and the faster convergence, lower residual errors, higher accuracy verified the improved EO algorithm would perform better than the original one.

Source: Journal of Physics Conference Series

DOI: 10.1088/1742-6596/1682/1/012054

Authors: Zheng-Ming Gao, Juan Zhao, Su-Ruo Li, Yu-Rong Hu

Institution: Jingchu University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Hubei Provincial Department of Education, Natural Science Foundation of Hubei Province

Awards: G1199328239, G2157880050, G8589665826