Maximum search limitations: Boosting evolutionary particle swarm optimization exploration

Mário Serra Neto, Marco Mollinetti, Vladimiro Miranda, Leonel Carvalho

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings
EditorsPaulo Moura Oliveira, Paulo Novais, Luís Paulo Reis
PublisherSpringer Verlag
Pages712-723
Number of pages12
ISBN (Print)9783030302405
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event19th EPIA Conference on Artificial Intelligence, EPIA 2019 - Vila Real, Portugal
Duration: 3 Sept 20196 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11804 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th EPIA Conference on Artificial Intelligence, EPIA 2019
Country/TerritoryPortugal
CityVila Real
Period3/09/196/09/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

  • Engineering design problems
  • Particle Swarm Optimization
  • Position-wise statistics
  • Swarm Intelligence

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