JOURNAL ARTICLE

Improved gravitational search algorithm based on chaotic local search

Zhaolu GuoWensheng ZhangShenwen Wang

Year: 2021 Journal:   International Journal of Bio-Inspired Computation Vol: 17 (3)Pages: 154-154   Publisher: Inderscience Publishers

Abstract

The traditional gravitational search algorithm (GSA) maintains good diversity of solutions but often demonstrates weak local search ability. To promote the local search ability of GSA, a new GSA based on chaotic local search (CLSGSA) is introduced in this paper. In its search operations, CLSGSA first executes the conventional search operations of the basic GSA to maintain the diversity of solutions. After that, CLSGSA executes a chaotic local search with the search experience from the current best solution to increase the local search capability. In the experiments, we utilise a suite of benchmark functions to verify the performance of CLSGSA. Moreover, we compare the proposed CLSGSA with several GSA variants. The comparisons validate the effectiveness of CLSGSA.

Keywords:
Gravitational search algorithm Chaotic Search algorithm Algorithm Computer science Local search (optimization) Artificial intelligence

Metrics

7
Cited By
0.71
FWCI (Field Weighted Citation Impact)
0
Refs
0.75
Citation Normalized Percentile
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Citation History

Topics

Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Robotic Path Planning Algorithms
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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