By Carlos Coello Coello, Clarisse Dhaenens, Laetitia Jourdan
The goal of this booklet is to assemble contributions that care for using nature encouraged metaheuristics for fixing multi-objective combinatorial optimization difficulties. this kind of assortment intends to supply an summary of the cutting-edge advancements during this box, with the purpose of motivating extra researchers in operations examine, engineering, and machine technology, to do examine during this sector. As such, this publication is predicted to develop into a necessary reference for these wishing to do study at the use of nature encouraged metaheuristics for fixing multi-objective combinatorial optimization problems.
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Extra resources for Advances in Multi-Objective Nature Inspired Computing
Wiley, USA (1998) 10. : The Pareto Envelope-based Selection Algorithm for Multiobjective Optimization. , Yao, X. ) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000) 11. : Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001) 12. : A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. , Yao, X. ) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000) 13. : A Fast and Elitist Multiobjective Genetic Algorithm: NSGA–II.
In: Congress on Evolutionary Computation (CEC 2002), Piscataway, New Jersey, May 2002, vol. 1, pp. 717–722. IEEE Service Center (2002) 63. : Multiobjective Evolutionary Algorithms: Classiﬁcations, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio (May 1999) 64. : On Measuring Multiobjective Evolutionary Algorithm Performance. In: 2000 Congress on Evolutionary Computation, Piscataway, New Jersey, July 2000, vol.
Hybridizing exact methods and metaheuristics: A taxonomy. European Journal of Operational Research 199(3), 620– 629 (2009) 36. : Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery. RAIRO Oper. Res (EDP Sciences) 42, 69–83 (2008) 1 Multi-Objective Combinatorial Optimization 19 37. : A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. In: Fifth International Conference on Intelligent Systems Design and Applications (ISDA 2005), pp.