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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
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
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review Shahab Wahhab Kareem; Shavan Askar; Roojwan Sc. Hawezi; Glena Aziz Qadir; Dina Yousif Mikhail
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 3 No 3 (2021): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA and IKATEMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v3i3.1

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.
Co-Authors Dina Yousif Mikhail Glena Aziz Qadir Roojwan Sc. Hawezi Shavan Askar