A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization

Shubham Gupta, Kusum Deep, Seyedali Mirjalili, Joong Hoon Kim

Research output: Contribution to journalArticle

Abstract

Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA.

Original languageEnglish
Article number113395
JournalExpert Systems With Applications
Volume154
DOIs
Publication statusPublished - 2020 Sep 15

Keywords

  • Algorithm
  • Benchmark
  • Engineering optimization problems
  • Exploration and exploitation
  • Genetic Algorithm
  • Grey Wolf Optimizer
  • Multilayer perceptron
  • Optimization
  • Particle Swarm Optimization
  • Sine Cosine Algorithm

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization'. Together they form a unique fingerprint.

  • Cite this