Multi-Modal Multi-Objective Evolutionary Optimization for Problems With Solutions Of Variable-Length

Amiram Moshaiov, Yosef Breslav, Eliran Farhi

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

10 Scopus citations

Abstract

This paper focuses on solving a special kind of multimodal multi-objective optimization problems (MMOPs) in which solutions are of variable length. First, problem definition and solution framework is suggested to allow using standard multimodal multi-objective evolutionary algorithms (MMEAs) to solve the considered type of problems. Next, a real-life example of the considered type of problems is suggested concerning optimal antennas' layout-allocation design for a wireless communication network. Finally, a modification to NSGA-II is suggested and employed to solve such layout problems. When compared with other MMEAs, it is shown that the proposed algorithm provides not only better solution diversity in the decision-space, but also solutions with superior performance vectors. It is suggested here that this is attributed to the type of archive that is used here.

Original languageEnglish
Title of host publication2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1193-1200
Number of pages8
ISBN (Electronic)9781728183923
DOIs
StatePublished - 2021
Event2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Virtual, Krakow, Poland
Duration: 28 Jun 20211 Jul 2021

Publication series

Name2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings

Conference

Conference2021 IEEE Congress on Evolutionary Computation, CEC 2021
Country/TerritoryPoland
CityVirtual, Krakow
Period28/06/211/07/21

Keywords

  • Location optimization
  • Multi-concept optimization
  • Multi-modal multiobjective optimization
  • Variable length problem, MMEAs
  • Variable number of dimensions
  • Wireless communication network

Fingerprint

Dive into the research topics of 'Multi-Modal Multi-Objective Evolutionary Optimization for Problems With Solutions Of Variable-Length'. Together they form a unique fingerprint.

Cite this