Artificial foraging weeds for global numerical optimization over continuous spaces

Invasive Weed Optimization (IWO) is a recently developed derivative-free metaheuristic algorithm that mimics the robust process of weeds colonization and distribution in an ecosystem. On the other hand central to an ecosystem is the foraging behavior that pertains to the act of searching for food and forms an integral part of the daily life of most of the living creatures. For over past two decades, a few significant optimization algorithms were developed by emulating the foraging behavior of creatures like ants, bacteria, fish, bees etc. This article presents a hybrid real-parameter optimizer developed by incorporating the principles of Optimal Foraging Theory (OFT) in IWO, with a view to improving the search mechanism of the latter over discontinuous and multi-modal fitness landscapes, riddled with local optima. The hybridization does not impose any serious computational burden on IWO in terms of increasing number of Function Evaluations (FEs). The performance of the resulting hybrid algorithm has been compared with eleven other state-of-the-art metaheuristic algorithms over a test-suite of 16 numerical benchmarks taken from the CEC (Congress on Evolutionary Computation) 2005 competition and special session on real parameter optimization. Our simulation experiments indicate that the proposed algorithm is able to attain comparable results against the nine other optimizers. Owing to its promising performance on benchmarks and ease of implementation (without requiring much programming overhead), the proposed algorithm may serve as an attractive alternative for a plethora of practical optimization problems.

[1]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[2]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[3]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[4]  Kalyanmoy Deb,et al.  A population-based, steady-state procedure for real-parameter optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  Daniel A. Ashlock,et al.  Evolutionary computation for modeling and optimization , 2005 .

[6]  Caro Lucas,et al.  A recommender system based on invasive weed optimization algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[8]  Alireza Mallahzadeh,et al.  DESIGN OF AN E-SHAPED MIMO ANTENNA USING IWO ALGORITHM FOR WIRELESS APPLICATION AT 5.8 GHZ , 2009 .

[9]  R. Macarthur,et al.  On Optimal Use of a Patchy Environment , 1966, The American Naturalist.

[10]  Jin Xu,et al.  Application of a novel IWO to the design of encoding sequences for DNA computing , 2009, Comput. Math. Appl..

[11]  Bijaya K. Panigrahi,et al.  On population variance and explorative power of invasive weed optimization algorithm , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[12]  Carlos García-Martínez,et al.  Hybrid real-coded genetic algorithms with female and male differentiation , 2005, 2005 IEEE Congress on Evolutionary Computation.

[13]  Miroslav L. Dukic,et al.  A Method of a Spread-Spectrum Radar Polyphase Code Design , 1990, IEEE J. Sel. Areas Commun..

[14]  B. Dadalipour,et al.  Application of the invasive weed optimization technique for antenna configurations , 2008, 2008 Loughborough Antennas and Propagation Conference.

[15]  Alireza Mallahzadeh,et al.  Compact U-array MIMO antenna designs using IWO algorithm , 2009 .

[16]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[17]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[18]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[19]  Pedro J. Ballester,et al.  Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX , 2005, 2005 IEEE Congress on Evolutionary Computation.

[20]  Aghil Yousefi-Koma,et al.  Optimal positioning of piezoelectric actuators on a smart fin using bio-inspired algorithms , 2007 .

[21]  Mirjana Cangalovic,et al.  Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search , 2003, Eur. J. Oper. Res..

[22]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[23]  Marcus Gallagher,et al.  Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA , 2005, 2005 IEEE Congress on Evolutionary Computation.

[24]  Joel s. Brown,et al.  Foraging : behavior and ecology , 2007 .

[25]  T. Caraco,et al.  Social Foraging Theory , 2018 .

[26]  Francisco Herrera,et al.  Adaptive local search parameters for real-coded memetic algorithms , 2005, 2005 IEEE Congress on Evolutionary Computation.

[27]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .