Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization

  title={Interactive Particle Swarm: A Pareto-Adaptive Metaheuristic to Multiobjective Optimization},
  author={Shubham Agrawal and Yogesh Dashora and Manoj Kumar Tiwari and Young-Jun Son},
  journal={IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans},
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… CONTINUE READING
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and J

  • B. V. Babu, P. G. Chakole
  • H. S. Mubeen, “Multiobjective differential…
  • 2005
Highly Influential
20 Excerpts

MOPSO: A proposal for multiple objective particle swarm optimization,

  • C. A. Coello Coello, M. S. Lechuga
  • in Proc. CEC,
  • 2002
Highly Influential
6 Excerpts

Multiobjective function optimization using nondominated sorting genetic algorithms,

  • N. Srinivas, K. Deb
  • Evol. Comput.,
  • 1995
Highly Influential
6 Excerpts

and C

  • H. A. Abbass, R. Sarkar
  • Newton, “PDE: A pareto-frontier differential…
  • 2001
Highly Influential
6 Excerpts

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