A hybrid version of invasive weed optimization with quadratic approximation

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• 作者：Y. Ramu Naidu ; A. K. Ojha
• 关键词：Invasive weed optimization ; Quadratic approximation ; Meta ; heuristic optimization technique ; Performance index
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：19
• 期：12
• 页码：3581-3598
• 全文大小：1,029 KB
• 参考文献：Abd-El-Waheda WF, Mousa AA, El-Shorbagy MA (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J Comput Appl Math 235:1446鈥?453MathSciNet CrossRef
  Ali M, Pant M, Singh VP (2010) Two modified differential evolution algorithms and their applications to engineering design problems. World J Model Simul 6(1):72鈥?0
  Barisal AK, Prusty RC (2015) Large scale economic dispatch of power systems using oppositional invasive weed optimization. Appl Soft Comput 29:122鈥?37CrossRef
  Basak A, Pal S, Das S, Abraham A (2010) Circular antenna array synthesis with a differential invasive weed optimization algorithm. In: 10th international conference on hybrid intelligent system, Atlanta, pp 153鈥?58
  Basak A, Maity D, Das S (2013) A differential invasive weed optimization algorithm for improved global numerical optimization. Appl Math Comput 219(12):6645鈥?668MATH MathSciNet CrossRef
  Chu SC, Tsai PW (2007) Computational intelligence based on the behavior of cats. Int J Innov Comput Inf Control 3:163鈥?73
  Dadalipour B, Mallahzadeh B, Davoodi-Rad Z (2008) Application of the invasive weed optimization technique for antenna configurations. In: 2008 Loughborough antennas and propagation vonference, pp 425鈥?28
  Das KN, Singh TK (2014) Drosophila food-search optimization algorithm. Appl Math Comput 231:566鈥?80MathSciNet CrossRef
  David RC, Precup RE, Petriu EM, Radac MB, Preitl S (2013) Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity. Inf Sci 247:154鈥?73MathSciNet CrossRef
  Deep K, Bansal JD (2009) Hybridization of particle swarm optimization with quadratic approximation. OPSEARCH 46(1):3鈥?4MATH MathSciNet CrossRef
  Deep K, Das KN (2008) Quadratic approximation based hybrid genetic algorithm for function optimization. Appl Math Comput 203:86鈥?8MATH CrossRef
  Deep K, Das KN (2009) Performance improvement of real coded genetic algorithm with quadratic approximation based hybridization. Int J Intell Def Support Syst 2(4):319鈥?34
  Deep K, Das KN (2013) A novel hybrid genetic algorithm for constrained optimization. Int J Syst Assur Eng Manag 4(1):86鈥?3CrossRef
  Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3鈥?8CrossRef
  El-Hefnawy NA (2014) Solving bi-level problems using modified particle swarm optimization algorithm. Int J Artif Intell 12(2):88鈥?01
  Eskandar H et al (2012) Water cycle algorithm鈥攁 novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110鈥?11:151鈥?66CrossRef
  Eusuff M, Lansey Kevin, Pasha Fayzul (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129鈥?54. doi:10.鈥?080/鈥?305215050038475鈥? MathSciNet CrossRef
  Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831鈥?845MATH MathSciNet CrossRef
  Hajimirsadeghi H, Lucas C (2009) A hybrid IWO/PSO algorithm for fast and global optimization. In: IEEE Eurocon, pp 1964鈥?971
  Javidya B, Hatamloua A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72鈥?9CrossRef
  Karimkashi S, Kishk AA (2010) Invasive weed optimization and its features in electromagnetic. IEEE Trans Antennas Propag 58(4):1269鈥?278CrossRef
  Kaveh A, Bakhashpoori T, Afshari E (2014) An efficient hybrid particle swarm optimization and swallow optimization algorithm. Comput Stuct 143:40鈥?9CrossRef
  Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, NJ, pp 1942鈥?948
  Li L, Wang X, Zhang Z, Fan L (2012) Convergence analysis of hybrid free search and invasive weed optimization algorithm. Appl Mech Mater 143鈥?44:329鈥?34CrossRef
  Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355鈥?66
  Mehrabian AR, Yousefi-Koma A (2007) Optimal positioning of piezoelectric actuators on a smart fin using bio-inspired algorithm. Aerosp Sci Technol 39:174鈥?82CrossRef
  Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst. doi:10.鈥?016/鈥媕.鈥媖nosys.鈥?015.鈥?7.鈥?06
  Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46鈥?1CrossRef
  Mohan C, Nguyen HT (1999) A controlled random search technique incorporating the simulating annealing concept for solving integer and mixed integer global optimization problems. Comput Optim Appl 14:103鈥?32MATH MathSciNet CrossRef
  Mohan C, Shanker K (1994) A random search technique for global optimization based on quadratic approximation. Asian Pac J Oper Res 11:93鈥?01MATH MathSciNet
  Nikoofard AH, Hajimirsadeghi H, Rahimi-Kian A, Lucas C (2012) Multiobjective invasive weed optimization: application to analysis of Pareto improvement models in electricity markets. Appl Soft Comput 12:100鈥?12CrossRef
  Pourjafari E, Mojallali H (2012) Solving nonlinear equations systems with a new approach based on invasive weed optimization algorithm and clustering. Swarm Evolut Comput 4:33鈥?3CrossRef
  Rad H, Lucas C (2007) A recommender system based on invasive weed optimization algorithm. In: IEEE congress on evolutionary computation, pp 4297鈥?304
  Shu-Kai SF, Zahara E (2007) A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur J Oper Res 181:527鈥?48MATH CrossRef
  Shu-Kai SF, Liang Y-C, Zahara E (2006) A genetic algorithm and a particle swarm optimizer hybridized with Nelder鈥揗ead simplex search. Comput Ind Eng 50:401鈥?25CrossRef
  Tsai JT, Liu T-K, Chou J-H (2004) Hybrid Taguchi algorithm for global numerical optimization. IEEE Trans 8(4):365鈥?77
  Valdez F, Melin P, Castillo O (2011) An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms. Appl Soft Comput 11:2625鈥?632
  Vivek KP, Vimal JS (2015) Heat transfer search (HTS): a novel optimization algorithm. Inf Sci. doi:10.鈥?016/鈥媕.鈥媔ns.鈥?015.鈥?6.鈥?44
  Wang L, Tang F, Wu H (2005) Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Appl Math Comput 171:1141鈥?156
  Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evolut Comput 1:67鈥?2
  Yang XS (2009) Firefly algorithms for multimodal optimization. In: Watanabe O, Zeugmann T (eds) Stochastic algorithms: foundations and applications, vol 5792. Lecture notes in Computer Science, Springer, Berlin, pp 169鈥?78
  Yang XS (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation, vol 7445. Lecture notes in Computer Science, Springer, Berlin, pp 240鈥?49
  Yang XS, Deb S (2009) Cuckoo search via Lvy flights. In: Nature and biologically inspired computing. NaBIC, World congress on IEEE, pp 210鈥?14
  Yazdani M, Jolai Fariborz (2015) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng. doi:10.鈥?016/鈥媕.鈥媕cde.鈥?015.鈥?6.鈥?03
  Zavoianu AC, Bramerdorfer G, Lughofer E, Silber S, Amrhein W, Klement EP (2013) Hybridization of multi-objective evolutionary algorithms and artificial neural networks for optimizing the performance of electrical drives. Eng Appl Artif Intell 26(8):1781鈥?794CrossRef
  Zhang G, Lu H (2006) Hybrid real-coded genetic algorithm with quasi-simplex technique. Int J Comput Sci Netw Secur 6(10):246鈥?55
  Zhou Y, Luo Q, Chen Huan (2013) A Novel Differential Evolution Invasive Weed Optimization Algorithm for Solving Nonlinear Equations Systems. Journal of Applied Mathematics. doi:10.鈥?155/鈥?013/鈥?57391
  
• 作者单位：Y. Ramu Naidu (1)
  A. K. Ojha (1)
  
  1. Indian Institute of Technology, Bhubaneswar, India
  
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Invasive weed optimization (IWO) is a recent meta-heuristic optimization technique, based on the life cycle of plants. It has been applied in many engineering applications as well as in real world problems. In this paper, a hybrid version of IWO with the quadratic approximation (QA) operator, referred as QAIWO, has been investigated to improve the convergence rate of IWO while obtaining optimal solution. Additionally, we alleviate the limitation of QA (which is nothing but difficulty in escaping from a local optimum) by performing QA a predetermined number of times and then considering the average of all such solutions due to each iteration rather than a single solution. This technique makes our algorithm more efficient compared to the existing algorithms in the area. Twenty two benchmark problems and five real-life problems are adopted from literature to validate our proposed hybrid method QAIWO. The results of QAIWO are compared with the results obtained by the standard IWO and the well-known nature-inspired genetic algorithm (GA). These comparisons exhibit that QAIWO is more convenient to solve complex problems than using IWO and/or GA. Keywords Invasive weed optimization Quadratic approximation Meta-heuristic optimization technique Performance index