Interactive particle swarm: A Pareto-adaptive metaheuristic to multiobjective optimization

Shubham Agrawal, Yogest Dashora, Manoj Tiwari, Young Jun Son

Research output: Contribution to journalArticle

83 Scopus citations

Abstract

This paper proposes an interactive particle-swarm metaheuristic for multiobjective optimization (MOO) that seeks to encapsulate the positive aspects of the widely used approaches, namely, Pareto dominance and interactive decision making in its solution mechanism. Pareto dominance is adopted as the criterion to evaluate the particles found along the search process. Nondominated particles are stored in an external repository which updates continuously through the adaptive-grid mechanism proposed. The approach is further strengthened by the incorporation of a self-adaptive mutation operator. A decision maker (DM) is provided with the knowledge of an approximate Pareto optimal front, and his/her preference articulations are used to derive a utility function intended to calculate the utility of the existing and upcoming solutions. The incubation of particle-swarm mechanism for the MOO by incorporating an adaptive-grid mechanism, a self-adaptive mutation operator, and a novel decision-making strategy makes it a novel and efficient approach. Simulation results on various test functions indicate that the proposed metaheuristic identifies not only the best preferred solution with a greater accuracy but also presents a uniformly diverse high utility Pareto front without putting excessive cognitive load on the DM. The practical relevance of the proposed strategy is very high in the cases that involve the simultaneous use of decision making and availability of highly favored alternatives.

Original languageEnglish (US)
Pages (from-to)258-277
Number of pages20
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume38
Issue number2
DOIs
StatePublished - Mar 1 2008

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Keywords

  • Metaheuristic
  • Multiobjective optimization (MOO)
  • Pareto dominance
  • Particle-swarm optimization (PSO)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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