An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems

详细信息    查看全文

• 作者：Jin Yi ; Liang Gao ; Xinyu Li ; Jie Gao
• 关键词：Harmony search ; Continuous optimization ; Intersect mutation operator ; Cellular local search
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：44
• 期：3
• 页码：725-753
• 全文大小：3,588 KB
• 参考文献：1.Boussaid I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117MathSciNet CrossRef MATH
  2.Holland JH (1975) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence
  3.Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359MathSciNet CrossRef MATH
  4.Zou DX, Wu JH, Gao LQ, Li S (2013) A modified differential evolution algorithm for unconstrained optimization problems. Neurocomputing 120:469–481CrossRef
  5.Kennedy J, Eberhart R (1995) Particle swarm optimization, proceedings of IEEE International Conference on neural networks (ICNN’95) in
  6.Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471MathSciNet CrossRef MATH
  7.Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents, Systems, Man, and Cybernetics. IEEE Transactions on Part B: Cybernetics 26:29–41
  8.Yang X-S, Deb S (2009) Cuckoo search via Lévy flights nature & biologically inspired computing, 2009. NaBIC World Congress on IEEE, pp 210–214
  9.Kirkpatrick S (1984) annealing, Optimization by simulated Quantitative studies. J Stat Phys 34:975–986MathSciNet CrossRef
  10.Lam AY, Li VO (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14:381–399CrossRef
  11.Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68CrossRef
  12.Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579MathSciNet CrossRef MATH
  13.Geem ZW (2009) Particle-swarm harmony search for water network design. Eng Optim 41:297–311CrossRef
  14.Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798CrossRef
  15.Panchal A (2009) Harmony search in therapeutic medical physics, in: Music-inspired Harmony search algorithm. Springer, pp 189–203
  16.Alia O, Mandava R, Ramachandram D, Aziz ME (2009) Dynamic fuzzy clustering using harmony search with application to image segmentation. In: Signal Processing and Information Technology (ISSPIT) IEEE International Symposium on IEEE, pp 538–543
  17.Zarei O, Fesanghary M, Farshi B, Saffar RJ, Razfar MR (2009) Optimization of multi-pass face-milling via harmony search algorithm. J Mater Process Tech 209:2386–2392CrossRef
  18.Liu L, Zhou H (2013) Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem. Inform Sci 226:68–92MathSciNet CrossRef MATH
  19.Cuevas E (2013) Block-matching algorithm based on harmony search optimization for motion estimation. Appl Intell 39:165–183CrossRef
  20.Arul R, Ravi G, Velusami S (2013) Chaotic self-adaptive differential harmony search algorithm based dynamic economic dispatch. Int J Electr Power Energy Syst 50:85–96CrossRef
  21.Kulluk S, Ozbakir L, Baykasoglu A (2012) Training neural networks with harmony search algorithms for classification problems. Eng Appl Artif Intell 25:11–19CrossRef
  22.Mun S, Cho YH (2012) Modified harmony search optimization for constrained design problems. Expert Systems with Applications 39:419–423CrossRef
  23.Chen J, Pan QK, Li JQ (2012) Harmony search algorithm with dynamic control parameters. Applied Mathematics and Computation 219:592–604MathSciNet CrossRef MATH
  24.Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198:643–656MathSciNet CrossRef MATH
  25.Wang L, Li LP (2013) An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems. Int J Electr Power Energy Syst 44:832–843CrossRef
  26.Banerjee A, Mukherjee V, Ghoshal S (2013) An opposition-based harmony search algorithm for engineering optimization problems. Ain Shams Eng J
  27.Wang H, Ouyang H, Gao L, Qin W (2014) Opposition-based learning harmony search algorithm with mutation for solving global optimization problems, In: Control and Decision Conference (2014 CCDC), The 26th Chinese, IEEE, pp 1090–1094
  28.Zou D, Gao L, Wu J, Li S (2010) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73:3308–3318CrossRef
  29.Im SS, Yoo DG, Kim JH (2013) Smallest-small-world cellular harmony search for optimization of unconstrained benchmark problems. J Appl Math:2013
  30.Al-Betar MA, Khader AT, Awadallah MA, Alawan MH, Zaqaibeh B (2013) Cellular Harmony Search for Optimization Problems. J Appl Math:2013
  31.Ashrafi SM, Dariane AB (2013) Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody Search (MS). Eng Appl Artif Intell 26:1301–1321CrossRef
  32.Al-Betar MA, Awadallah MA, Khader AT, Abdalkareem ZA (2015) Island-based harmony search for optimization problems. Expert Systems with Applications 42:2026–2035CrossRef
  33.Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216:830–848MathSciNet CrossRef MATH
  34.Yadav P, Kumar R, Panda SK, Chang CS (2012) An Intelligent Tuned Harmony Search algorithm for optimisation. Inform Sci 196:47–72CrossRef
  35.Kattan A, Abdullah R (2013) A dynamic self-adaptive harmony search algorithm for continuous optimization problems. Appl Math Comput 219:8542–8567MathSciNet CrossRef MATH
  36.Enayatifar R, Yousefi M, Abdullah AH, Darus AN (2013) LAHS: a novel harmony search algorithm based on learning automata. Commun Nonlinear Sci 18:3481–3497MathSciNet CrossRef
  37.Kumar V, Chhabra JK, Kumar D (2014) Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. J Comput Sci-Neth 5:144–155MathSciNet CrossRef
  38.Zhou YZ, Li XY, Gao L (2013) A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 13:390–401CrossRef
  39.Schiff JL (2011) Cellular automata: a discrete view of the world. Wiley
  40.Alba E, Dorronsoro B (2005) The exploration/exploitation tradeoff in dynamic cellular genetic algorithms. IEEE Trans Evol Comput 9:126–142CrossRef
  41.Alba E, Alfonso H, Dorronsoro B (2005) Advanced models of cellular genetic algorithms evaluated on SAT, Gecco. Genetic and Evolutionary Computation Conference 1 and 2:1123–1130
  42.Shi Y, Liu HC, Gao L, Zhang GH (2011) Cellular particle swarm optimization. Inform Sci 181:4460–4493MathSciNet CrossRef MATH
  43.Im SS, Yoo DG, Kim JH (2013) Smallest-Small-World Cellular Harmony Search for Optimization of Unconstrained Benchmark Problems
  44.Al-Betar MA, Khader AT, Awadallah MA, Alawan MH, Zaqaibeh B (2013) Cellular Harmony Search for Optimization Problems
  45.Yang X-S, Deb S (2010) Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation 1:330–343CrossRef MATH
  46.Yang X-S, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40:1616–1624MathSciNet CrossRef
  47.Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. Ieee T Evolut Comput 3:82–102CrossRef
  48.Liang J, Qu B, Suganthan P, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212
  49.Zhu J, Chew DA, Lv S, Wu W (2013) Optimization method for building envelope design to minimize carbon emissions of building operational energy consumption using orthogonal experimental design (OED). Habitat International 37:148–154CrossRef
  50.Rice J (2006) Mathematical statistics and data analysis. Cengage Learning
  51.Castelli M, Silva S, Manzoni L, Vanneschi L (2014) Geometric Selective Harmony Search. Inform Sci 279:468–482MathSciNet CrossRef
  52.Zambrano-Bigiarini M, Clerc M, Rojas R (2013) Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements. In: Evolutionary Computation (CEC), IEEE Congress on IEEE, pp 2337–2344
  53.Nepomuceno FV, Engelbrecht AP (2013) A self-adaptive heterogeneous pso for real-parameter optimization. In: Evolutionary Computation (CEC), IEEE Congress on IEEE, pp 361–368
  54.Qin A, Li X (2013) Differential evolution on the CEC-2013 single-objective continuous optimization testbed. In: Evolutionary Computation (CEC), IEEE Congress on IEEE, pp 1099–1106
  55.Zhang C, Gao L (2013) An effective improvement of JADE for real-parameter optimization. In: Advanced Computational Intelligence (ICACI), Sixth International Conference on IEEE, pp 58–63
  56.Tanabe R, Fukunaga A (2013) Evaluating the performance of SHADE on CEC 2013 benchmark problems. In: Evolutionary Computation (CEC) IEEE Congress on IEEE, pp 1952–1959
  
• 作者单位：Jin Yi (1)
  Liang Gao (1)
  Xinyu Li (1)
  Jie Gao (1)
  
  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China
  
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497

文摘

This paper proposes a modified harmony search (MHS) algorithm with an intersect mutation operator and cellular local search for continuous function optimization problems. Instead of focusing on the intelligent tuning of the parameters during the searching process, the MHS algorithm divides all harmonies in harmony memory into a better part and a worse part according to their fitness. The novel intersect mutation operation has been developed to generate new -harmony vectors. Furthermore, a cellular local search also has been developed in MHS, that helps to improve the optimization performance by exploring a huge search space in the early run phase to avoid premature, and exploiting a small region in the later run phase to refine the final solutions. To obtain better parameter settings for the proposed MHS algorithm, the impacts of the parameters are analyzed by an orthogonal test and a range analysis method. Finally, two sets of famous benchmark functions have been used to test and evaluate the performance of the proposed MHS algorithm. Functions in these benchmark sets have different characteristics so they can give a comprehensive evaluation on the performance of MHS. The experimental results show that the proposed algorithm not only performs better than those state-of-the-art HS variants but is also competitive with other famous meta-heuristic algorithms in terms of the solution accuracy and efficiency.