from 01.01.2024 until now Russian Federation
Many applied optimization problems are characterized by the presence of several contradictory criteria, as well as changing conditions over time. Such tasks are called Dynamic Multi-objective Optimization (DMOO) tasks. A classic example is route planning in a transportation network, where the weights of the edges (travel time, cost, risk) may vary due to traffic jams, weather conditions, or other factors. Evolutionary algorithms such as NSGA-II and SPEA2 have proven themselves well for solving multi-criteria problems. However, their behavior in dynamic conditions has not been sufficiently studied. The purpose of this paper is to compare the effectiveness of these algorithms with different strategies for responding to changes.
multi-criteria optimization, optimization in a non-stationary environment, evolutionary algorithms, genetic algorithms
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