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Soft Computing
Published in: Soft Computing 21/2019

24-04-2019 | Foundations

A hybrid many-objective cuckoo search algorithm

Authors: Zhihua Cui, Maoqing Zhang, Hui Wang, Xingjuan Cai, Wensheng Zhang

Published in: Soft Computing | Issue 21/2019

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Abstract

Cuckoo search (CS) is an excellent population-based algorithm and has shown promising performance in dealing with single- and multi-objective optimization problems. However, for many-objective optimization problems (MaOPs), CS cannot be directly employed. So far, few paper have been reported to use CS to solve MaOPs. In this paper, we try to propose a hybrid many-objective cuckoo search (HMaOCS) for MaOPs. In HMaOCS, the standard CS is firstly modified to effectively deal with MaOPs. Then, non-dominated sorting and the strategy of reference points are employed to ensure the convergence and diversity. In order to verify the performance of HMaOCS, DTLZ and WFG benchmark sets are utilized in the experiments. Experimental results show that HMaOCS can achieve promising performance compared with five other well-known many-objective optimization algorithms.
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Literature
Abdel-Baset M, Zhou Y, Ismail M (2018) An improved cuckoo search algorithm for integer programming problems. Int J Comput Sci Math 9(1):66–81MathSciNetCrossRef
Adra S, Fleming P (2011) Diversity management in evolutionary many-objective optimization. IEEE Trans Evol Comput 15(2):183–195CrossRef
Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76CrossRef
Barthelemy P, Bertolotti J, Wiersma D (2008) A Lévy flight for light. Nature 453(7194):495
Cai X, Gao X, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-inspired Comput 8(4):205–214CrossRef
Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping strategy for numerical optimization. Int J Mach Learn Cybernet 9(2):199–215CrossRef
Chandrasekaran K, Simon S (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16CrossRef
Coelho L, Guerra F, Batistela N (2013) Multiobjective cuckoo search algorithm based on duffing’s oscillator applied to jiles-atherton vector hysteresis parameters estimation. IEEE Trans Magn 49(5):1745–1748CrossRef
Cortés P, Muñuzuri J, Onieva L, Guadix J (2018) A discrete particle swarm optimisation algorithm to operate distributed energy generation networks efficiently. Int J Bio-Inspired Comput 12(4):226–235CrossRef
Cui Z, Sun B, Wang G, Xue Y, Chen J (2017b) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems. J Parallel Distrib Comput 103:42–52CrossRef
Cui Z, Xue F, Cai X, Cao Y, Wang G, Chen J (2018) Detection of malicious code variants based on deep learning. IEEE Trans Industr Inf 14(7):3187–3196CrossRef
Cui Z, Du L, Wang P, Cai X, Zhang W (2019a) Malicious code detection based on CNNs and multi-objective algorithm. J Parallel Distrib Comput 129:50–58CrossRef
Cui Z, Li F, Zhang W (2019b) Bat algorithm with principal component analysis. Int J Mach Learn Cybernet 10(3):603–622CrossRef
Das I, Dennis J (2006) Normal-Boundary Intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Optim 8(3):631–657MathSciNetMATHCrossRef
Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601CrossRef
Deb K, Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New YorkMATH
Deb K, Thiele L, Laumanns M, Zitzler E (2002a) Scalable multi-objective optimization test problems. In: Proceedings of the 2002 congress on IEEE evolutionary computation. CEC ‘02, pp 825–830
Deb K, Pratap A, Agarwal S, Meyarivan T (2002b) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef
Deb K, Mohan M, Mishra S (2005) Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–525CrossRef
Fan J, Li Y, Tang L, Wu G (2018) RoughPSO: rough set-based particle swarm optimization. Int J Bio-Inspired Comput 12(4):245–253CrossRef
Hanoun S, Nahavandi S, Creighton D, Kull H (2012) Solving a multiobjective job shop scheduling problem using Pareto archived cuckoo search. Emerg Technol Factory Autom IEEE 43:1–8
Huband S, Hingston P, Barone L, While L (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evol Comput 10(5):477–506MATHCrossRef
Hughes E (2003) Multiple single objective Pareto sampling. In: The 2003 congress on IEEE evolutionary computation (CEC), vol 4, pp 2678–2684
Jain H, Deb K (2014) An Evolutionary Many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18(4):602–622CrossRef
Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi- objective optimization. MIT Press, Cambridge, pp 263–282
Li M, Zheng J (2009) Spread assessment for evolutionary multi-objective optimization. In: International conference on evolutionary multi-criterion optimization, Springer, pp 216–230
Li M, Zheng J, Li K, Yuan Q, Shen R (2010) Enhancing diversity for average ranking method in evolutionary many-objective optimization. In: Parallel problem solving from nature, PPSN XI. Springer, Berlin, pp. 647–656
Li M, Yang S, Liu X (2014) Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput 18(3):348–365CrossRef
Niu Y, Tian Z, Zhang M, Cai X, Li J (2018) Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction. Int J Comput Sci Math 9(6):547–554MathSciNetCrossRef
Pandey H, Chaudhary A, Mehrotra D (2018) Bit mask-oriented genetic algorithm for grammatical inference and premature convergence. Int J Bio-Inspired Comput 12(1):54–69CrossRef
Pooja P, Chaturvedi P, Kumar P, Tomar A (2018) A novel differential evolution approach for constraint optimization. Int J Bio-Inspired Comput 12(4):254–265CrossRef
Raja B, Jhala R, Patel V (2017) Many-objective optimization of cross-flow plate-fin heat exchanger. Int J Therm Sci 118:320–339CrossRef
Rani K, Malek M, Neoh S (2013) Hybrid multiobjective optimization using modified cuckoo search algorithm in linear array synthesis. In: IEEE antennas and propagation conference, pp 1–4
Reynolds AM, Frye MA (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS ONE 2(4):e354CrossRef
Shan X, Ye B, Zhang L (2018) Analysis of flow field of hydrodynamic suspension polishing disk based on multi-fractal method. Int J Comput Sci Math 9(1):13–20MathSciNetCrossRef
Sun B, Cui Z, Dai C (2014) DV-hop localization algorithm with cuckoo search. Sensor Lett 12(2):444–447CrossRef
Wang Z, Li Y (2015) Irreversibility analysis for optimization design of plate fin heat exchangers using a multi-objective cuckoo search algorithm. Energy Convers Manag 101:126–135CrossRef
Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, Cui Z (2017) Firefly algorithm with neighborhood attraction. Inf Sci 382(383):374–387CrossRef
Wang H, Wang W, Cui Z, Zhou X, Zhao J, Li Y (2018) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95–106MathSciNetCrossRef
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRef
Yang X, Deb S (2010a) Cuckoo search via Lévy flights. In: Nature and biologically inspired computing 2009, NaBIC 2009, world congress on IEEE, pp 210–214
Yang X, Deb S (2010b) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343MATH
Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 17(5):721–736CrossRef
Yigit T, Unsal O, Deperlioglu O (2018) Using the metaheuristic methods for real-time optimisation of dynamic school bus routing problem and an application. Int J Bio-Inspired Comput 11(2):123–133CrossRef
Zhang QF, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731CrossRef
Zhang X, Tian Y, Jin Y (2015) A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 19(6):761–776CrossRef
Zhang M, Wang H, Cui Z, Chen J (2018) Hybrid multi-objective cuckoo search with dynamical local search. Memet Comput 10(2):199–208CrossRef
Zhao B, Xue Y, Xu B, Ma T, Liu J (2018) Multi-objective classification based on NSGA-II. Int J Comput Sci Math 9(6):539–546MathSciNetCrossRef
Zhou X, Liu Y, Li B (2016) A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks. Mod Phys Lett B 30(07):1650080MathSciNetCrossRef
Zitzler E, Kunzli S (2004) Indicator-based selection in multi objective search. In: Lecture notes in computing science, pp 832–842
Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou K, et al. (eds) EUROGEN 2001. International Center for Numerical Methods in engineering (CIMNE), pp 95–100
Zou X, Chen Y, Liu M, Kang L (2008) A new evolutionary algorithm for solving many-objective optimization problems. IEEE Trans Syst Man Cybern B Cybern 38(5):1402–1412CrossRef
Metadata
Title
A hybrid many-objective cuckoo search algorithm
Authors
Zhihua Cui
Maoqing Zhang
Hui Wang
Xingjuan Cai
Wensheng Zhang
Publication date
24-04-2019
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 21/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-019-04004-4

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