Evolutionary multi-objective optimization (EMO), also called evolutionary multi-criterion optimization, is the application of evolutionary algorithms to multi-objective optimization problems. Such problems contain two or more objectives that are optimized simultaneously and generally give rise to a set of trade-off solutions rather than a single optimum.
Evolutionary methods are particularly suited to generating finite approximations of the Pareto front because a population can contain several candidate solutions representing different trade-offs at the same time. A typical EMO algorithm therefore attempts to improve convergence toward the Pareto front while maintaining diversity among the solutions.
Most EMO methods follow an a posteriori approach in which an approximation set is generated before a final solution is selected by a decision maker. Preference information can, however, also be incorporated during the evolutionary search.
Emmerich, Michael T. M.; Deutz, André H. (2018).
"A tutorial on multiobjective optimization: fundamentals and evolutionary methods".
Natural Computing.
17 (3):
585–609.
doi:
10.1007/s11047-018-9685-y.
PMC 6105305.
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↑ Deb, Kalyanmoy; Pratap, Amrit; Agarwal, Sameer; Meyarivan, T. (2002). "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II". IEEE Transactions on Evolutionary Computation. 6 (2): 182–197. doi:10.1109/4235.996017.
↑ Beume, Nicola; Naujoks, Boris; Emmerich, Michael (2007). "SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume". European Journal of Operational Research. 181 (3): 1653–1669. doi:10.1016/j.ejor.2006.08.008.
↑ Deb, Kalyanmoy; Jain, Himanshu (2014). "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints". IEEE Transactions on Evolutionary Computation. 18 (4): 577–601. doi:10.1109/TEVC.2013.2281535.
1 2 3 4 5 6 Purshouse, Robin C.; Fleming, Peter J. (2003). "Evolutionary Many-Objective Optimisation: An Exploratory Analysis". Proceedings of the 2003 Congress on Evolutionary Computation. Vol. 3. IEEE. pp. 2066–2073. doi:10.1109/CEC.2003.1299927.
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