Multi-objective optimization with artificial weed colonies

  • Authors:

  • Debarati Kundu;Kaushik Suresh;Sayan Ghosh;Swagatam Das;B. K. Panigrahi;Sanjoy Das

  • Affiliations:

  • Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, India;Dept. of Electrical Engineering, Indian Institute of Technology, Delhi, India;Dept. of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66502, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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Abstract

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization.