Gaussian guided self-adaptive wolf search algorithm
Authors
Author (Corporation)
Publication date
2018
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Entropy
Special issue
Information theory in machine learning and data science
DOI of the original publication
Link
Series
Series number
Volume
20
Issue / Number
1
Pages / Duration
Patent number
Publisher / Publishing institution
MDPI
Place of publication / Event location
Basel
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Nowadays, swarm intelligence algorithms are becoming increasingly popular for solving many optimization problems. The Wolf Search Algorithm (WSA) is a contemporary semi-swarm intelligence algorithm designed to solve complex optimization problems and demonstrated its capability especially for large-scale problems. However, it still inherits a common weakness for other swarm intelligence algorithms: that its performance is heavily dependent on the chosen values of the control parameters. In 2016, we published the Self-Adaptive Wolf Search Algorithm (SAWSA), which offers a simple solution to the adaption problem. As a very simple schema, the original SAWSA adaption is based on random guesses, which is unstable and naive. In this paper, based on the SAWSA, we investigate the WSA search behaviour more deeply. A new parameter-guided updater, the Gaussian-guided parameter control mechanism based on information entropy theory, is proposed as an enhancement of the SAWSA. The heuristic updating function is improved. Simulation experiments for the new method denoted as the Gaussian-Guided Self-Adaptive Wolf Search Algorithm (GSAWSA) validate the increased performance of the improved version of WSA in comparison to its standard version and other prevalent swarm algorithms.
Keywords
Subject (DDC)
330 - Wirtschaft
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
1099-4300
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
SONG, Qun, Simon FONG, Suash DEB und Thomas HANNE, 2018. Gaussian guided self-adaptive wolf search algorithm. Entropy. 2018. Bd. 20, Nr. 1. DOI 10.3390/e20010037. Verfügbar unter: https://doi.org/10.26041/fhnw-6486