Evolutional solutions by using PSO for 0-1 combinatorial optimization problems with constraints

Naoaki Ogawa, Eitaro Aiyoshi

Research output: Contribution to journalArticle

Abstract

In this paper, as one of global optimization methods for 0-1 combinatorial optimization problems with constraints, a continuous relaxation approach is presented, in which the continuous variables are transformed into binary variables through a sorting procedure of continuous variables taking the constraints into consideration. The new type of relaxation approach enables us to apply Particle Swarm Optimization, which is effective heuristic method for global optimization with continuous variables. Here, our presented approach is interpreted as one of evolutional computing methods because the transformation of continuous variables into binary ones corresponds to transform genotype into phenotype, which is reverse to a relation in usual evolutional computing.

Original languageEnglish
Pages (from-to)1136-1143
Number of pages8
JournalIEEJ Transactions on Electronics, Information and Systems
Volume132
Issue number7
DOIs
Publication statusPublished - 2012

Fingerprint

Combinatorial optimization
Global optimization
Particle swarm optimization (PSO)
Heuristic methods
Sorting

Keywords

  • 0-1 combinatorial optimization problems
  • Allocation type equality constraints
  • Evolutional computing
  • Global optimization
  • Knapsack type inequality constraints

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Evolutional solutions by using PSO for 0-1 combinatorial optimization problems with constraints. / Ogawa, Naoaki; Aiyoshi, Eitaro.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 132, No. 7, 2012, p. 1136-1143.

Research output: Contribution to journalArticle

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