Multi-objective particle swarm optimization

Seyedali Mirjalili, Jin Song Dong

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Swarm Intelligence (SI) refers to the collective behaviour of a group of creatures without a centralized unit control. This field was first established in 1989 in a robotic project [1]. Systems built based on SI typically have independent intelligent agents that interact locally to achieve a goal as a team [2]. Most of the algorithms in this field mimic swarm intelligence in nature. For instance, Ant Colony Optimization (ACO) [3] mimics swarm intelligence of ants in an ant colony using stigmergy, which is the communication between individuals in a swarm by modifying environment. It has been proved that ants can find the shortest path between multiple path to a food course from their nest by a depositing and marking the ground using pheromone.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
PublisherSpringer Verlag
Pages21-36
Number of pages16
DOIs
Publication statusPublished - 1 Jan 2020

Publication series

NameSpringerBriefs in Applied Sciences and Technology
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

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  • Cite this

    Mirjalili, S., & Dong, J. S. (2020). Multi-objective particle swarm optimization. In SpringerBriefs in Applied Sciences and Technology (pp. 21-36). (SpringerBriefs in Applied Sciences and Technology). Springer Verlag. https://doi.org/10.1007/978-3-030-24835-2_3