Abstract
Most of multiobjective optimization algorithms consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, in MOPs, different objective functions may possess different properties. Hence, it can be beneficial to build objective-wise optimization strategy for each objective separately. In this paper, we firstly propose a single objective guided multiobjective optimization (SOGMO) framework to solve continuous MOPs. In SOGMO framework, a solution is selected from archive, and then objective-wise learning strategy is developed to promote the evolution of each objective of the selected solution. Thus, all the objectives of the considered solution can be simultaneously optimized in parallel by the cooperation of objective-wise learning process. A specific instantiation of the SOGMO framework is implemented, where a neighborhood field optimization (NFO) algorithm, as objective-wise learning strategy, and \(\epsilon\) dominance archive are designed. The proposed SOGMO implementation, called SOGMO-NFO, is systematically compared with several state-of-the-art multiobjective evolutionary algorithms (MOEA). Simulation results on 13 benchmark problems from CEC 2009 competition show that SOGMO-NFO is better than the compared MOEAs.
Similar content being viewed by others
References
Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19(1):45–76
Bandyopadhyay S, Maulik U, Chakraborty R (2013) Incorporating \(\epsilon\)-dominance in AMOSA: application to multiobjective 0/1 knapsack problem and clustering gene expression data. Appl Soft Comput 13(5):2405–2411
Bosman P (2012) On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization. IEEE Trans Evol Comput 16(1):51–69
Burke EK, Silva JDL (2006) The influence of the fitness evaluation method on the performance of multiobjective search algorithms. Eur J Oper Res 169(3):875–897
Chen X, Li J, Susilo W (2012) Efficient fair conditional payments for outsourcing computations. IEEE Trans Inform Forensics Secur 7(6):1687–1694
Chen X, Li J, Ma J, Tang Q, Lou W (2013) New algorithms for secure outsourcing of modular exponentiations. IEEE Trans Parallel Distributed Syst. doi:10.1109/TPDS.2013.180
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197
Deb K, Mohan M, Mishra S (2005) Evaluating the \(\epsilon\)-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–25
Derrac J, García 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 Evol Comput 1(1):3–18
Grobler J, Engelbrecht A (2009) Hybridizing PSO and DE for improved vector evaluated multi-objective optimization. In: Proceeding of IEEE congress on computational intelligence, pp 1255–1262
Hadka D, Reed PM, Simpson TW (2012) Diagnostic assessment of the borg MOEA for many-objective product family design problems. In: Proceedings of the 2012 IEEE congress on evolutionary computation, pp 1–10
Han Y, Young P, Zimmerle D (2013) Microgrid generation units optimum dispatch for fuel consumption minimization. J Ambient Intell Humanized Comput. doi:10.1007/s12652-012-0158-3
Harrison K, AP Engelbrecht BOB (2013) Knowledge transfer strategies for vector evaluated particle swarm optimization. In: EMO 2013, LNCS 7811, pp 171–184
Hsu WH, Chiang TC (2012) A multiobjective evolutionary algorithm with enhanced reproduction operators for the vehicle routing problem with time windows. In: Proceedings of the 2012 IEEE congress on evolutionary computation, pp 1–8
Ishibuchi H, Narukawa K, Tsukamoto N, Nojima Y (2008) An empirical study on similarity-based mating for evolutionary multiobjective combinatorial optimization. Eur J Oper Res 188(1):57–75
Jordehi A, Jasni J (2013) Particle swarm optimization for discrete optimization problems: a review. Artif Intell Rev. doi:10.1007/s10462-012-9373-8
Kafafy A, Bounekkar A, Bonnevay S (2012) Hybrid metaheuristics based on moea/d for 0/1 multiobjective knapsack problems: a comparative study. In: Proceeding of IEEE congress on computational intelligence, pp 1–8
Kern S, Mller SD, et al (2004) Learning probability distributions in continuous evolutionary algorithmsca comparative review. Nat Comput 3(1):77–112
Kowatari N, Oyama A, Aguirre HE, Tanaka K (2012) A study on large population moea using adaptive \(\epsilon\)-box dominance and neighborhood recombination for many objective optimization. In: LION 6, LNCS 7219, pp 86–100
Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263–82
Li J, Kim K (2010) Hidden attribute-based signatures without anonymity revocation. Inform Sci 180(9):1681–1689
Li J, Wang Q, Wang C, Cao N, Ren K, Lou W (2010) Fuzzy keyword search over encrypted data in cloud computing. In: proceeding of the 29th IEEE international conference on computer communications, pp 441–445
Li J, Chen X, Li J, Jia C, Ma J, Lou W (2013a) Fine-grained access control based on outsourced attribute-based encryption. In: proceeding of the European symposium on research in computer security, LNCS 3184, pp 592–609
Li M, Liu L, Lin D (2011) A fast steady-state \(\epsilon\)-dominance multi-objective evolutionary algorithm. Comput Optim Appl 48(1):109–138
Li M, Yang S, Liu X (2013b) Shift-based density estimation for pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput. doi:10.1109/TEVC.2013.2262178
Martino FD, Sessa S (2013) A fuzzy particle swarm optimization algorithm and its application to hotspot events in spatial analysis. J Ambient Intell Humaniz Comput 4(1):85–97
Mei Y, Tang K, Yao X (2011) A memetic algorithm for periodic capacitated arc routing problem. IEEE Trans Syst Man Cybern Part B 41(6):1654–1667
Mininno E, Neri F, Cupertino F, Naso D (2011) Compact differential evolution. IEEE Trans Evol Comput 15(1):17–29
Mokryani G, Siano P, Piccolo A (2013) Optimal allocation of wind turbines in microgrids by using genetic algorithm. J Ambient Intell Humaniz Comput. doi:10.1007/s12652-012-0163-6
Molina D, Lozano M, Garcia-Martinez C, Herrera F (2010) Memetic algorithms for continuous optimization based on local search chains. Evol Comput 18(1):27–63
Neri F, Iacca G, EMininno (2011) Disturbed exploitation compact differential evolution for limited memory optimization problems. Inform Sci 181(12):2469–2487
Norman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125
Oda T, Barolli A, Xhafa F, Barolli L, Ikeda M, Takizawa M (2013) WMN-GA: a simulation system for WMN s and its evaluation considering selection operators. J Ambient Intell Humaniz Comput 4(3):323–330
Qi R, Du W, Wang Z, Qian F (2008) Multiobjective evolutionary algorithm based on the Pareto archive and individual migration. In: Proceedings of the 7th World congress on intelligent control and automation, pp 4489–4495
Sato H, Aguirre HE, Tanaka K (2013) Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems. Ann Math Artif Intell. doi:10.1007/s10472-012-9293-y
Srivastava V, Tripathi BK, Pathak VK (2013) Biometric recognition by hybridization of evolutionary fuzzy clustering with functional neural networks. J Ambient Intell Humaniz Comput. doi:10.1007/s12652-012-0161-8
Talbi EG, Basseur M, Nebro A, Alba E (2012) Multi-objective optimization using metaheuristics: non-standard algorithms. Int Trans Oper Res 19(1-2):283–305
Tricoire F (2012) Multi-directional local search. Comp Oper Res 39(12):3089–3101
Wang J, Zhong C, Zhou Y (2013) Single objective guided multiobjective optimization algorithm. In: Proceeding of fourth international conference on emerging intelligent data and web technologies, pp 178–183
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1(6):80–83
Wu Z, Chow TWS (2012) A local multiobjective optimization algorithm using neighborhood field. Struct Multidiscip Optim 45(6):853–870
Wu Z, Chow TWS (2013) Neighborhood field for cooperative optimization. Soft Comput 17(5):819–834
Yang S, Li M, Liu X, Zheng J (2013) A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 17(5):721–736
Zhan Z, Li J, Cao J, Zhang J (2013) Multiple populations for multiple objectives: a co-evolutionary technique for solving multi-objective optimization problems. IEEE Trans Syst Man Cybern Part B: Cybern 43(2):445–463
Zhang Q, Sun J, Tsang E (2005) Evolutionary algorithm with guided mutation for the maximum clique problem. IEEE Trans Evol Comput 9(2):192–200
Zhang Q, Member S, Li H (2007) MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731
Zhang Q, Zhou A, Jin Y (2008) RM-MEDA: A regularity model based multiobjective estimation of distribution algorithm. IEEE Trans Evol Comput 12(1):41–63
Zhang Q, Liu W, Li H (2009a) The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proceedings of the 2009 IEEE congress on evolutionary computation, pp 203–208
Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2009b) Multiobjective optimization test instances for the CEC 2009 special session and competition. In: Proceedings of the 2009 IEEE congress on evolutionary computation, pp 1–30
Zhang Y, Gong DW, Ding ZH (2011) Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer. Expert Syst Appl 38(11):13,933–13,941
Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: A survey of the state-of-the-art. Swarm Evol Comput 1(1):23–49
Zlochin M, Birattari M, Meuleau N, Dorigo M (2004) Model-based search for combinatorial optimization: A critical survey. Annals Oper Res 131(1-4):373–395
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (60805026,61070076), and the Zhujiang New Star of Science and Technology in Guangzhou City (2011J2200093, 2012J2200085).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, J., Zhong, C., Zhou, Y. et al. Multiobjective optimization algorithm with objective-wise learning for continuous multiobjective problems. J Ambient Intell Human Comput 6, 571–585 (2015). https://doi.org/10.1007/s12652-014-0218-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-014-0218-y