Multi-objective optimization with artificial weed colonies

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.

[1]  Sanjoy Das,et al.  Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models , 2004, GECCO.

[2]  P. Fleming,et al.  Convergence Acceleration Operator for Multiobjective Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[3]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[4]  Sanghamitra Bandyopadhyay,et al.  Multiobjective GAs, quantitative indices, and pattern classification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  A. Mallahzadeh,et al.  Application of the Invasive Weed Optimization Technique for Antenna Configurations , 2008 .

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

[7]  Kalyanmoy Deb,et al.  Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

[8]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[9]  Jonathan E. Fieldsend,et al.  Multi-objective optimisation in the presence of uncertainty , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[11]  Janez Brest,et al.  Differential Evolution with Self-adaptation and Local Search for Constrained Multiobjective Optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[12]  Ponnuthurai N. Suganthan,et al.  Multi-objective evolutionary programming without non-domination sorting is up to twenty times faster , 2009, 2009 IEEE Congress on Evolutionary Computation.

[13]  Yujia Wang,et al.  Particle swarm optimization with preference order ranking for multi-objective optimization , 2009, Inf. Sci..

[14]  Qingfu Zhang,et al.  Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[15]  Mario Köppen,et al.  Fuzzy-Pareto-Dominance and its Application in Evolutionary Multi-objective Optimization , 2005, EMO.

[16]  Lakhmi C. Jain,et al.  Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[17]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[18]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[19]  Evan J. Hughes.  Multi-objective evolutionary guidance for swarms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[20]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[21]  Yuping Wang,et al.  A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

[23]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[24]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[25]  Hsiang-Cheh Huang,et al.  A refactoring method for cache-efficient swarm intelligence algorithms , 2012, Inf. Sci..

[26]  Marco Laumanns,et al.  A Tutorial on Evolutionary Multiobjective Optimization , 2004, Metaheuristics for Multiobjective Optimisation.

[27]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

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

[29]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[30]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .

[31]  Efrn Mezura-Montes,et al.  Constraint-Handling in Evolutionary Optimization , 2009 .

[32]  Jonathan E. Fieldsend,et al.  Dominance measures for multi-objective simulated annealing , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[34]  Marco Farina,et al.  A fuzzy definition of "optimality" for many-criteria optimization problems , 2004, IEEE Trans. Syst. Man Cybern. Part A.

[35]  Jouni Lampinen,et al.  Performance assessment of Generalized Differential Evolution 3 with a given set of constrained multi-objective test problems , 2009, 2009 IEEE Congress on Evolutionary Computation.

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

[37]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[38]  Sanjoy Das,et al.  A Multiobjective Evolutionary-Simplex Hybrid Approach for the Optimization of Differential Equation Models of Gene Networks , 2008, IEEE Transactions on Evolutionary Computation.

[39]  Qingfu Zhang,et al.  Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition , 2009 .

[40]  L. Jain,et al.  Evolutionary multiobjective optimization : theoretical advances and applications , 2005 .

[41]  E. J. Hughes,et al.  Constraint handling with uncertain and noisy multi-objective evolution , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).