Article: Constrained optimisation and robust function optimisation with EIWO Journal: International Journal of Bio-Inspired Computation (IJBIC) 2013 Vol.5 No.2 pp.84 - 98 Abstract: A robust variant of invasive weed optimisation (IWO) algorithm, called enhanced invasive weed optimisation (EIWO) algorithm, is proposed in this paper for the optimisation of constrained benchmark problems. Enjoying the ecological behaviour of colonising weeds, IWO has demonstrated its ability in solving different optimisation problems. Since making a proper balance between these two components is essential, especially to cope with constraint optimisation problems, two new rules are added to the algorithm to improve its performance. The first rule is utilising principles of social standard deviation as proposed in social harmony search (SHS) algorithm. The second rule is utilised to prevent the algorithm to get stuck on local optima. Finally, for constraint handling, three simple heuristic rules of Deb are utilised. The robustness and effectiveness of the proposed method are tested on many constrained benchmark problems and compared against those of state-of-the-art algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: Constrained optimisation and robust function optimisation with EIWO

Authors: Pezhman Ramezani; Milad Ahangaran; Xin-She Yang

Addresses: Department of Industrial Engineering, K.N. Toosi University of Technology, 470 Mirdamad Ave. West, 19697, Tehran, Iran ' Department of Civil Engineering, Iran University of Science and Technology, 1684613114 Narmak, Tehran, Iran ' School of Science and Technology, Middlesex University, London NW4 4BT, UK; School of Engineering, Reykjavik University, Menntavegi 1 101, Reykjavik, Iceland

Abstract: A robust variant of invasive weed optimisation (IWO) algorithm, called enhanced invasive weed optimisation (EIWO) algorithm, is proposed in this paper for the optimisation of constrained benchmark problems. Enjoying the ecological behaviour of colonising weeds, IWO has demonstrated its ability in solving different optimisation problems. Since making a proper balance between these two components is essential, especially to cope with constraint optimisation problems, two new rules are added to the algorithm to improve its performance. The first rule is utilising principles of social standard deviation as proposed in social harmony search (SHS) algorithm. The second rule is utilised to prevent the algorithm to get stuck on local optima. Finally, for constraint handling, three simple heuristic rules of Deb are utilised. The robustness and effectiveness of the proposed method are tested on many constrained benchmark problems and compared against those of state-of-the-art algorithms.

Keywords: metaheuristics; enhanced IWO; invasive weed optimisation; EIWO; social harmony search; constrained optimisation; social spatial dispersion; randomisation; robust function optimisation; constraint handling; bio-inspired computation.

DOI: 10.1504/IJBIC.2013.053505

International Journal of Bio-Inspired Computation, 2013 Vol.5 No.2, pp.84 - 98

Received: 05 Nov 2012
Accepted: 05 Jan 2013
Published online: 31 Mar 2014 *

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Title: Constrained optimisation and robust function optimisation with EIWO

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