Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction |
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Affiliation: | 1. Department of Energy Resources Engineering, College of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea;2. Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78758, United States;3. Department of Civil and Environmental Engineering & Water Resources Research Center, University of Hawai''i at Manoa, Honolulu, HI 96822, United States;4. Department of Climate and Energy Systems Engineering, Ewha Womans Univesity, 52 Ewhayeodae-gil, Daehyeon-dong, Seodaemun-gu, Seoul 03760, Republic of Korea |
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Abstract: | This study investigates the coupling effects of objective-reduction and preference-ordering schemes on the search efficiency in the evolutionary process of multi-objective optimization. The difficulty in solving a many-objective problem increases with the number of conflicting objectives. Degenerated objective space can enhance the multi-directional search toward the multi-dimensional Pareto-optimal front by eliminating redundant objectives, but it is difficult to capture the true Pareto-relation among objectives in the non-optimal solution domain. Successive linear objective-reduction for the dimensionality-reduction and dynamic goal programming for preference-ordering are developed individually and combined with a multi-objective genetic algorithm in order to reflect the aspiration levels for the essential objectives adaptively during optimization. The performance of the proposed framework is demonstrated in redundant and non-redundant benchmark test problems. The preference-ordering approach induces the non-dominated solutions near the front despite enduring a small loss in diversity of the solutions. The induced solutions facilitate a degeneration of the Pareto-optimal front using successive linear objective-reduction, which updates the set of essential objectives by excluding non-conflicting objectives from the set of total objectives based on a principal component analysis. Salient issues related to real-world problems are discussed based on the results of an oil-field application. |
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Keywords: | Objective-reduction Preference-ordering Evolutionary process Many-objective problem Pareto-optimal front |
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