Multi-objective particle swarm optimization

Seyedali Mirjalili; Jin Song Dong

doi:10.1007/978-3-030-24835-2_3

Multi-objective particle swarm optimization

Seyedali Mirjalili
, Jin Song Dong

Centre for Artificial Intelligence Research and Optimisation (AIRO)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

6 Citations (Scopus)

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 language	English
Title of host publication	SpringerBriefs in Applied Sciences and Technology
Publisher	Springer Verlag
Pages	21-36
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-030-24835-2_3
Publication status	Published - 1 Jan 2020

Publication series

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action
SDG 17 Partnerships for the Goals

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10.1007/978-3-030-24835-2_3

Cite this

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ER -

Multi-objective particle swarm optimization

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UN SDGs

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