Free Access
RAIRO-Oper. Res.
Volume 51, Number 4, October-December 2017
Page(s) 1301 - 1315
Published online 27 November 2017
  • M. Ali, P. Siarry and M. Pant, An efficient differential evolution based algorithm for solving multi-objective optimization problems. Europ. J. Oper. Res. 217 (2012) 404–416. [Google Scholar]
  • H. Abbass, R. Sarker and C. Newton, PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. Evolutionary Computation. Proc. of the 2001 Congress 2 (2001) 971–978. [CrossRef] [Google Scholar]
  • J. Bader and E. Zitzler, HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Comput. 19 (2011) 45–76. [CrossRef] [Google Scholar]
  • S. Bandyopadhyay and A. Mukherjee, An Algorithm for Many-Objective Optimization with Reduced Objective Computations: A Study in Differential Evolution. IEEE Trans. Evolut. Comput. 19 (2015) 400–413. [CrossRef] [Google Scholar]
  • M. Basseur, A. Liefooghe and K. Le, The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems. J. Heuristics 18 (2012) 263–296. [CrossRef] [Google Scholar]
  • D.W. Corne and J.D. Knowles, Techniques for highly multiobjective optimisation: some nondominated points are better than others. Proc. of the 9th annual conference on Genetic and evolutionary computation. ACM (2007) 773–780. [Google Scholar]
  • O. Chikumbo, E. Goodman and K. Deb, Approximating a multi-dimensional pareto front for a land use management problem: A modified moea with an epigenetic silencing metaphor. IEEE Congress on Evolutionary Computation. IEEE (2012) 1–9. [Google Scholar]
  • K. Deb, A. Pratap and S. Agarwal, A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation. IEEE Trans. 6 (2002) 182–197. [Google Scholar]
  • S. Das and P.N. Suganthan, Differential evolution: a survey of the state-of-the-art. Evolutionary Computation. IEEE Trans. 15 (2011) 4–31. [Google Scholar]
  • K. Deb, L. Thiele and M. Laumanns, Scalable multi-objective optimization test problems. Proc. of the Congress on Evolutionary Computation (CEC-2002), Honolulu, USA (2002) 825–830. [Google Scholar]
  • K. Deb and D.K. Saxena, On finding pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. Kangal report (2005) 2005011. [Google Scholar]
  • G. Fu, Z. Kapelan and J.R. Kasprzyk, Optimal design of water distribution systems using many-objective visual analytics. J. Water Resources Plan. Manag. 139 (2012) 624–633. [CrossRef] [Google Scholar]
  • S. Jiang and Z. Cai, A new differential evolution for multiobjective optimization by uniform design and minimum reduce hypervolume. Natural Computing. Springer Jpn (2010) 199–208. [Google Scholar]
  • X. He, C. Dai and Z. Chen, Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method. Math. Probl. Eng. (2014) 259–473. [Google Scholar]
  • I. Giagkiozis, R.C. Purshouse and P.J. Fleming, An overview of population-based algorithms for multi-objective optimisation. Inter. J. Syst. Sci. 46 (2015) 1572–1599. [CrossRef] [Google Scholar]
  • H. Li and Q. Zhang, Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. Evolut. Comput. IEEE Trans. 13 (2009) 284–302. [CrossRef] [Google Scholar]
  • K. Narukawa and T. Rodemann, Examining the performance of evolutionary many-objective optimization algorithms on a real-world application[C]. Genetic and Evolutionary Computing (ICGEC). Sixth Inter. Confer. IEEE (2012) 316–319. [Google Scholar]
  • A.J. Nebro, J.J. Durillo and J. Garcia−Nieto, Smpso: A new pso-based metaheuristic for multi-objective optimization. Computational intelligence in miulti-criteria decision-making. MCDM’09. IEEE Symposium (2009) 66–73. [Google Scholar]
  • R.C. Purshouse and P.J. Fleming, On the evolutionary optimization of many conflicting objectives. Evolutionary Computation. IEEE Trans. 11 (2007) 770–784. [Google Scholar]
  • K.E. Parsopoulos, D.K. Tasoulis and N.G. Pavlidis, Vector evaluated differential evolution for multiobjective optimization. IEEE Congress on Evolutionary Computation (2004) 204–211. [Google Scholar]
  • A. Ponsich, A.L. Jaimes and C. Coello, A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. Evolutionary Computation. IEEE Trans. 17 (2013) 321–344. [Google Scholar]
  • R.G.D. Steel, J.H. Torrie and D.A. Dickey, Principles and procedures of statistics a biometrical approach. WCB/McGraw-Hill (1997). [Google Scholar]
  • R. Storn and K. Price, Differential evolution Ca simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimiz. 11 (1997) 341–359. [CrossRef] [MathSciNet] [Google Scholar]
  • H. Ishibuchi, N. Tsukamoto and Y. Nojima, Evolutionary many-objective optimization. Genetic and Evolving Systems. GEFS. 3rd International Workshop on IEEE (2008) 47–52. [Google Scholar]
  • T. Wagner, N. Beume and B. Naujoks, Pareto-, aggregation-, and indicator-based methods in many-objective optimization. Evolutionary multi-criterion optimization. Springer Berlin/Heidelberg (2007) 742–756. [Google Scholar]
  • S. Kukkonen and J. Lampinen, GDE3: The third evolution step of generalized differential evolution. Evolutionary Computation. IEEE Congress on Evolutionary Computation 1 (2005) 443–450. [Google Scholar]
  • G. Wu, R. Mallipeddi and P.N. Suganthan, Differential evolution with multi-population based ensemble of mutation strategies. Inform. Sci. 329 (2016) 329–345. [CrossRef] [Google Scholar]
  • M. Črepinšek, S.H. Liu and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR) 45 (2013) 35. [Google Scholar]
  • E. Zitzler, L. Thiele and J. Bader, On set-based multiobjective optimization. IEEE Trans. Evolut. Comput. 14 (2010) 58–79. [CrossRef] [Google Scholar]
  • E. Zitzler and S. Knzli, Indicator-based selection in multiobjective search. Parallel Problem Solving from Nature-PPSN VIII. Springer Berlin Heidelberg (2004) 832–842 [Google Scholar]
  • E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications. Ithaca: Shaker (1999). [Google Scholar]
  • E. Zitzler, L. Thiele and M. Laumanns, Performance assessment of multiobjective optimizers: an analysis and review. Evolut. Comput. IEEE Trans. 7 (2003) 117–132. [CrossRef] [Google Scholar]
  • A. Zhou, B. YQu and H. Li, Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Comput. 1 (2011) 32–49. [CrossRef] [Google Scholar]
  • E. Zitzler, M. Laumanns and L. Thiele, Improving the performance of the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH), Zurich (2001). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.