Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/124219
Title: EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS
Authors: GEE SEN BONG
Keywords: multi-objective, optimization, evolutionary algorithm, dynamic, detection
Issue Date: 14-Jan-2016
Citation: GEE SEN BONG (2016-01-14). EVOLUTIONARY MULTI-OBJECTIVE OPTIMIZATION IN STATIC AND DYNAMIC ENVIRONMENTS. ScholarBank@NUS Repository.
Abstract: Although many works have been reported to solve the static multi-objective optimization problems (MOPs), there are few studies focusing on online solution diversity assessment. The diversity information is useful for selection process and optimizer's parameters tuning. An online diversity loss assessment method is presented and implemented on the decomposition-based multi-objective optimization algorithm to solve the numerical benchmark problems and vehicle routing problems with stochastic demands. Despite the increasing number of dynamic multi-objective optimization related research works, the optimization performance benchmarking and change detection issue are two research gaps which have received less attention. A novel dynamic multi-objective benchmark problem generator is suggested to study the performance of the optimizer under time-varying fitness landscape modality, trade-off connectedness and Pareto degeneracy problems. For the change detection issue, a two-stage change detection approach using inverse modelling approach is presented to reduce the number of fitness evaluations used for detection purpose.
URI: http://scholarbank.nus.edu.sg/handle/10635/124219
Appears in Collections:Ph.D Theses (Open)

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