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Soft Computing
Published in: Soft Computing 20/2019

29-10-2018 | Methodologies and Application

Improved harmony search with general iteration models for engineering design optimization problems

Authors: Haibin Ouyang, Wenqiang Wu, Chunliang Zhang, Steven Li, Dexuan Zou, Guiyun Liu

Published in: Soft Computing | Issue 20/2019

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Abstract

Harmony search (HS) algorithm has a strong exploration and exploitation capability based on its unique improvisation. However, little research has been done on its improvisation mechanism. This paper offers a detailed discussion on the HS improvisation mechanism, which aims to state that the improvisation is a generic search framework. To improve the performance of HS, global learning strategy is designed to enhance the global search capability, and modified random selection is used to reduce the possibility of falling into local optimum. Moreover, a new improvement perspective such as the adjustment of iteration model is presented in this paper. Different iteration models such as dimension-to-dimension mode, stochastic multi-dimensional mode, vector mode and matrix mode to explore the optimization potential of HS algorithm are employed. Combining the improved operations, parameter adjustments and the four iteration models, four improved HS variants are proposed to analyze the effectiveness of iteration model on HS algorithm. Experimental results demonstrate the proposed HS algorithms can yield significant improved performance. Overall, the paper shows that the HS improvisation framework has a good extensibility and the iteration model has significant impact on the performance of HS.
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Appendix
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Literature
Aguirre H, Zavala AM, Diharce EV et al (2007) COPSO: constrained optimization via PSO algorithm. Technical report No. I-07-04/22-02-2007, Center for Research in Mathematics (CIMAT), 2007
Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014CrossRef
Akhtar S, Tai K, Ray T (2002) A socio-behavioral simulation model of engineering design optimization. Eng Optim 34:341–354CrossRef
Akin A, Saka MP (2015) Harmony search algorithm based optimum detailed design of reinforced concrete plane frames subject to ACI 318-05 provisions. Comput Struct 147:79–95CrossRef
Alatas B (2010) Chaotic harmony search algorithms. Appl Math Comput 216(9):2687–2699MATH
Ali MZ, Awad NH, Suganthan PN (2015) Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization. Appl Soft Comput 33:304–327CrossRef
Baykasoğlu A (2012) Design optimization with chaos embedded great deluge algorithm. Appl Soft Comput 12:1055–1567CrossRef
Baykasoğlu A, Ozsoydan FB (2015) Adaptive firefly algorithm with chaos for mechanical design optimization problems. Appl Soft Comput 36:152–164CrossRef
Belegundu AD, Arora JS (1985) A study of mathematical programming methods for structural optimization. Part I: theory. Int J Numer Methods Eng 21(9):1583–1599MATHCrossRef
Brajevic I, Tuba M (2013) An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J Intell Manuf 24:729–740CrossRef
Cagnina L, Esquivel S, Coello CC (2008) Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32(3):319–326MATH
Cobos C, Estupiñán D, Pérez J (2011) GHS + LEM: global-best harmony search using learnable evolution models. Appl Math Comput 218:2558–2578MathSciNetMATH
Coeiho LS (2009) An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications. Reliab Eng Syst Saf 94(4):830–837CrossRef
Coello CAC (1999) Self-adaptive penalties for GA-based optimization. In: Proceedings of the 1999 Congress on evolutionary computation, 1999. CEC 99, vol 1. IEEE
Coello CAC, Montes EM (2002) Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inf 16(3):193–203CrossRef
Das S, Mukhopadhyay A, Roy A et al (2011) Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization. IEEE Trans Syst Man Cybern Part B Cybern 41:89–106CrossRef
De Melo VCV, Carosio GLC (2013) Investigating multi-view differential evolution for solving constrained engineering design problems. Expert Syst Appl 40(9):3370–3377CrossRef
Eberhart RC, Kennedy J (1995) Particle swarm optimization. In: Proceeding of IEEE international conference on neural network. Perth, Australia, pp 1942–1948
Enayatifar R, Yousefi M, Abdullah AH et al (2013) LAHS: a novel harmony search algorithm based on learning automata. Commun Nonlinear Sci Numer Simul 18:3481–3497MathSciNetMATHCrossRef
Fesanghary M, Mahdavi M, Minary-Jolandan M et al (2008) Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Methods Appl Mech Eng 197:3080–3091MATHCrossRef
Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89:2325–2336CrossRef
Gandomi AH, Yang X-S, Alavi AH et al (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255CrossRef
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68CrossRef
Gen M, Yun Y (2006) Soft computing approach for reliability optimization: state-of-the-art survey. Reliab Eng Syst Saf 91(9):1008–1026CrossRef
Gen M, Ida K, Kobuchi R et al (1998) Hybridized neural network and genetic algorithms for solving nonlinear integer programming. In: lain LC, 1ain RK (eds) 1998 Second international conference on knowledge-based intelligent electronic systems, 21–23 April 1998, Adelaide, Aushalia, pp 272–277
Guo Z, Wang S, Yue X et al (2017) Global harmony search with generalized opposition-based learning. Soft Comput 21(8):2129–2137CrossRef
Guo Z, Yang H, Wang S et al (2018) Adaptive harmony search with best-based search strategy. Soft Comput 22(4):1335–1349CrossRef
He Q, Wang L (2007a) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20(1):89–99CrossRef
He Q, Wang L (2007b) A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput 186:1407–1422MathSciNetMATH
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Hsieh YC, Chen TC et al (1998) Genetic algorithms for reliability design problems. Microelectron Reliab 38(10):1599–1605CrossRef
Huang F, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATH
Jaddi NS, Abdullah S (2017) A cooperative-competitive master-slave global-best harmony search for ANN optimization and water-quality prediction. Appl Soft Comput 51:209–224CrossRef
Kanagaraj G, Ponnambalam SG, Jawahar N (2013) A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems. Comput Ind Eng 66(4):1115–1124CrossRef
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetMATHCrossRef
Keshtegar B, Sadeq MO (2017) Gaussian global-best harmony search algorithm for optimization problems. Soft Comput 21(24):7337–7349CrossRef
Khalili M, Kharrat R, Salahshoor K et al (2014) Global dynamic harmony search algorithm: GDHS. Appl Math Comput 228:195–219MathSciNetMATH
Kong X, Gao L, Ouyang H et al (2015) Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Comput Oper Res 63:7–22MathSciNetMATHCrossRef
Kulluk S (2013) A novel hybrid algorithm combining hunting search with harmony search algorithm for training neural networks. J Oper Res Soc 64:748–761CrossRef
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933MATHCrossRef
Li Y, Li X, Gupta JND (2015) Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search. Expert Syst Appl 42(3):1409–1417CrossRef
Li X, Qin K, Zeng B et al (2017) A dynamic parameter controlled harmony search algorithm for assembly sequence planning. Int J Adv Manuf Technol 92(9–12):3399–3411CrossRef
Liao TW (2010) Two hybrid differential evolution algorithms for engineering design optimization. Appl Soft Comput 10(4):1188–1199CrossRef
Luus R (1975) Optimization of system reliability by a new nonlinear integer programming procedure. IEEE Trans Reliab R-24(1):14–16CrossRef
Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579MathSciNetMATH
Manjarre D, Landa-Torres I, Gil-Lopez S et al (2013) A survey on applications of the harmony search algorithm. Eng Appl Artif Intell 26(8):1818–1831CrossRef
Maruta I, Kim TH, Sugie T (2009) Fixed-structure H∞ controller synthesis: a metaheuristic approach using simple constrained particle swarm optimization. Automatica 45:553–559MathSciNetMATHCrossRef
Moh’d Alia O, Mandava R (2011) The variants of the harmony search algorithm: an overview. Artif Intell Rev 36(1):49–68CrossRef
Ouyang H, Gao L, Li S, Kong X et al (2014) On the iterative convergence of harmony search algorithm and a proposed modification. Appl Math Comput 247:1064–1095MathSciNetMATH
Ouyang H, Gao L, Li S et al (2015) Improved novel global harmony search with a new relaxation method for reliability optimization problems. Inf Sci 305:14–55CrossRef
Ouyang HB, Gao LQ, Kong XY, Li S, Zou DX (2016) Hybrid harmony search particle swarm optimization with global dimension selection. Inf Sci 346:318–337CrossRef
Ouyang H, Gao L, Li S et al (2017) Improved harmony search algorithm: LHS. Appl Soft Comput 53:133–167CrossRef
Ouyang H, Gao L, Li S (2018) Amended harmony search algorithm with perturbation strategy for large-scale system reliability problems. Appl Intell 2018:1–26
Pan QK, Suganthan PN, Tasgetiren MF et al (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216:830–848MathSciNetMATH
Pourvaziri H, Naderi B (2014) A hybrid multi-population genetic algorithm for the dynamic facility layout problem. Appl Soft Comput 24:457–469CrossRef
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRef
Ratnaweera A, Halgamuge S, Watson H (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8:240–255CrossRef
Ravi V, Murty BSN, Reddy PJ (1997) Nonequilibrium simulated annealing-algorithm applied to reliability optimization of complex systems. IEEE Trans Reliab 46(2):233–239CrossRef
Reddy SS (2018) Optimal power flow using hybrid differential evolution and harmony search algorithm. Int J Mach Learn Cybern 2018:1–15
Sadollah A, Sayyaadi H, Yoo DG et al (2018) Mine blast harmony search: a new hybrid optimization method for improving exploration and exploitation capabilities. Appl Soft Comput 68:548–564CrossRef
Sheikhalishahi M, Ebrahimipour V et al (2013) A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem. Int J Adv Manuf Technol 2013:1–22
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetMATHCrossRef
Tomassetti G (2009) A cost-effective algorithm for the solution of engineering problems with particle swarm optimization. Eng Optim 42:471–495CrossRef
Valaei MR, Behnamian J (2017) Allocation and sequencing in 1-out-of-N heterogeneous cold-standby systems: multi-objective harmony search with dynamic parameters tuning. Reliab Eng Syst Saf 7(157):78–86CrossRef
Valian E, Tavakoli S, Mohanna S et al (2013) Improved cuckoo search for reliability optimization problems. Comput Ind Eng 64(1):459–468CrossRef
Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37:2826–2837CrossRef
Wang L, Li L (2012) A coevolutionary differential evolution with harmony search for reliability–redundancy optimization. Expert Syst Appl 39(5):5271–5278CrossRef
Wang L, Li L (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
Wang Y, Cai Z, Zhou Y et al (2009) Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique. Struct Multidiscip Optim 37(4):395–413CrossRef
Wang L, Hu H, Liu R et al (2018) An improved differential harmony search algorithm for function optimization problems. Soft Comput 2018:1–26
Wu P, Gao L, Zou D et al (2011) An improved particle swarm optimization algorithm for reliability problems. ISA Trans 50(1):71–81CrossRef
Wu B, Qian C, Ni W et al (2012) Hybrid harmony search and artificial bee colony algorithm for global optimization problems. Comput Math Appl 64:2621–2634MathSciNetMATHCrossRef
Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRef
Yeh WC, Hsieh TJ (2011) Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Comput Oper Res 38(11):1465–1473MathSciNetCrossRef
Yildiz AR (2013) Comparison of evolutionary-based optimization algorithms for structural design optimization. Eng Appl Artif Intell 26:327–333CrossRef
Yokota T, Gen M, Li HH (1996) Genetic algorithm for nonlinear mixed-integer programming problems and its application. Comput Ind Eng 30(4):905–917CrossRef
Zhai J, Gao L, Li S (2015) Robust pole assignment in a specified union region using harmony search algorithm. Neurocomputing 155:12–21CrossRef
Zhan ZH, Zhang J, Li Y et al (2009) Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern Part B Cybern 39:1362–1381CrossRef
Zhang JQ, Sanderson A (2009) JADE: Adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13:945–958CrossRef
Zou DX, Gao LQ, Wu JH et al (2010a) Novel global harmony search algorithm for unconstrained problems. Neurocomputing 73(16–18):3308–3318CrossRef
Zou D, Gao L, Wu J et al (2010b) A novel global harmony search algorithm for reliability problems. Comput Ind Eng 58(2):307–316CrossRef
Zou D, Gao L, Li S et al (2011) An effective global harmony search algorithm for reliability problems. Expert Syst Appl 38(4):4642–4648CrossRef
Metadata
Title
Improved harmony search with general iteration models for engineering design optimization problems
Authors
Haibin Ouyang
Wenqiang Wu
Chunliang Zhang
Steven Li
Dexuan Zou
Guiyun Liu
Publication date
29-10-2018
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 20/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3579-x

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