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By Lam Thu Bui, Sameer Alam
Multi-objective optimization (MO) is a fast-developing box in computational intelligence examine. Giving determination makers extra innovations to choose between utilizing a few post-analysis choice details, there are various aggressive MO options with an more and more huge variety of MO real-world purposes. Multi-Objective Optimization in Computational Intelligence: concept and perform explores the theoretical, in addition to empirical, functionality of MOs on a variety of optimization matters together with combinatorial, real-valued, dynamic, and noisy difficulties. This ebook presents students, lecturers, and practitioners with a basic, complete choice of examine on multi-objective optimization strategies, functions, and practices.
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MIT Press. Ehrgott, M. (2005). ). Berlin, Germany: Springer. Fishburn, P. C. (1974). Lexicographic orders, utilities, and decision rules: A survey. Management Science, 20(11), 1442-1471. Fonseca, C. & Fleming, P. (1993). Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California (pp. 416-423). Morgan Kauffman Publishers. Fonseca, C. & Fleming, P. (1996). On the performance assessement and comparision of stochastic multi-objective optimizers.
EMOO repository. cinvestav. mx/ ccoello/EMOO/ Coello, C. A. C. (2006b). Evolutionary multiobjective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. Coello, C. A. , Pulido, G. , & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions one Evolutionary Computation, 8(3), 256-279. Coello, C. A. , Veldhuizen, D. A. , & Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems.
International Center for Numerical Methods in Engineering (Cmine). Zitzler, E. & Thiele, L. (1998). Multi-objective optimization using evolutionary algorithms—A comparative case study. Parallel problem solving from nature, Lecture notes in computer science (Vol. 1498, pp. 292-304). Springer. Zitzler, E. & Thiele, L. (1999). Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271. , & Deb, K.