A stability constrained adaptive alpha for gravitational search algorithm

Genyun Sun, Ping Ma, Jinchang Ren, Aizhu Zhang, Xiuping Jia

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton’s law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha ( α ) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same α value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent’s evolutionary state is estimated, which is then combined with the variation of the agent’s position and fitness feedback to adaptively adjust the value of α. Moreover, to preserve agents’ stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do.
LanguageEnglish
Pages200-213
Number of pages14
JournalKnowledge Based Systems
Volume139
Early online date20 Oct 2017
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Heuristic algorithms
Gravitation
Trajectories
Feedback
Exploration and exploitation
Evolutionary
Fitness
Interaction
Imbalance
Optimization problem
Factors
Peers
Trajectory
Heuristic algorithm
Metaheuristics
Gravity

Keywords

  • meta-heuristic algorithm
  • gravitational search algorithm (GSA)
  • adaptive parameter
  • stability conditions
  • exploration and exploitation

Cite this

Sun, Genyun ; Ma, Ping ; Ren, Jinchang ; Zhang, Aizhu ; Jia, Xiuping. / A stability constrained adaptive alpha for gravitational search algorithm. In: Knowledge Based Systems. 2018 ; Vol. 139. pp. 200-213.
@article{7a47b2f5f2a94cbe824bd7f22891b85f,
title = "A stability constrained adaptive alpha for gravitational search algorithm",
abstract = "Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton’s law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha ( α ) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same α value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent’s evolutionary state is estimated, which is then combined with the variation of the agent’s position and fitness feedback to adaptively adjust the value of α. Moreover, to preserve agents’ stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do.",
keywords = "meta-heuristic algorithm, gravitational search algorithm (GSA), adaptive parameter, stability conditions, exploration and exploitation",
author = "Genyun Sun and Ping Ma and Jinchang Ren and Aizhu Zhang and Xiuping Jia",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.knosys.2017.10.018",
language = "English",
volume = "139",
pages = "200--213",
journal = "Knowledge Based Systems",
issn = "0950-7051",

}

A stability constrained adaptive alpha for gravitational search algorithm. / Sun, Genyun; Ma, Ping; Ren, Jinchang; Zhang, Aizhu; Jia, Xiuping.

In: Knowledge Based Systems, Vol. 139, 01.01.2018, p. 200-213.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A stability constrained adaptive alpha for gravitational search algorithm

AU - Sun, Genyun

AU - Ma, Ping

AU - Ren, Jinchang

AU - Zhang, Aizhu

AU - Jia, Xiuping

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton’s law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha ( α ) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same α value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent’s evolutionary state is estimated, which is then combined with the variation of the agent’s position and fitness feedback to adaptively adjust the value of α. Moreover, to preserve agents’ stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do.

AB - Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton’s law of gravity and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational constant attenuation factor alpha ( α ) plays a vital role in convergence and the balance between exploration and exploitation. However, in GSA and most of its variants, all agents share the same α value without considering their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability constrained adaptive alpha for GSA (SCAA). In SCAA, each agent’s evolutionary state is estimated, which is then combined with the variation of the agent’s position and fitness feedback to adaptively adjust the value of α. Moreover, to preserve agents’ stable trajectories and improve convergence precision, a boundary constraint is derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13 conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that SCAA has significantly better searching performance than its peers do.

KW - meta-heuristic algorithm

KW - gravitational search algorithm (GSA)

KW - adaptive parameter

KW - stability conditions

KW - exploration and exploitation

UR - https://www.sciencedirect.com/science/journal/09507051

U2 - 10.1016/j.knosys.2017.10.018

DO - 10.1016/j.knosys.2017.10.018

M3 - Article

VL - 139

SP - 200

EP - 213

JO - Knowledge Based Systems

T2 - Knowledge Based Systems

JF - Knowledge Based Systems

SN - 0950-7051

ER -