Work

An improved self-adaptive bat algorithm

Year: 2017

Type: conference-paper

Abstract: Considering the problems of low solution precision and suffering from pre-mature convergence by the basic bat algorithm, an improved self-adaptive bat algorithm (SABA) is proposed after the introduction of the self-adaptive step-controlled mechanism and mutation mechanism in this paper.The results of the proposed algorithm based on 6 test functions indicate that compared with the particle swarm algorithm and the basic bat algorithm, the SABA algorithm effectively avoid falling into the local optimum in the early stage and had the higher solution accuracy.

Source: International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE)

DOI: 10.2991/icmmcce-17.2017.273

Authors: Shilei Lyu, Yonglin Huang, Zhen Li, Yueju Xue

Institution: South China Agricultural University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Science and Technology Planning Project of Guangdong Province

Awards: G2316690310, G5548650603, G7671550806, G8595170770