A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review

  • Shahab Wahhab Kareem Information System Engineering Department, Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq; Department of Information Technology, Catholic University in Erbil, Kurdistan Iraq
  • Shavan Askar Information System Engineering Department, Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq
  • Roojwan Sc. Hawezi Information System Engineering Department, Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq
  • Glena Aziz Qadir Information System Engineering Department, Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq
  • Dina Yousif Mikhail Information System Engineering Department, Technical Engineering College, Erbil Polytechnic University, Erbil, Iraq; Department of Information Technology, Catholic University in Erbil, Kurdistan Iraq
Keywords: Swarm Intelligence, Optimization, Particle Swarm Optimization, Ant Colony Optimization

Abstract

Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. We see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores some of the methods and algorithms.

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Published
2021-10-04
How to Cite
[1]
S. W. Kareem, S. Askar, R. S. Hawezi, G. A. Qadir, and D. Y. Mikhail, “A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review”, j.electron.electromedical.eng.med.inform, vol. 3, no. 3, pp. 111-118, Oct. 2021.
Section
Articles