Dividing variables into groups is an intuitive idea for tackling large-scalemulti-objective problems. However, regular grouping methods often suffer from the computationally expensive budget, resulting in the inflexi...
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Dividing variables into groups is an intuitive idea for tackling large-scalemulti-objective problems. However, regular grouping methods often suffer from the computationally expensive budget, resulting in the inflexibility of the division of variables. To remedy this issue, this paper proposes a Pearson correlation-based adaptive variable grouping method, which not only consumes no additional computational budget, but also is able to adaptively divide variables with the evolvement of solutions. According to our observation, variables with similar effects on objectives exhibit similar evolutionary trends. Therefore, the Pearson correlation coefficient is used to measure the similarities of the evolutionary trends of variables. Based on the Pearson correlation-based adaptive variable grouping method, this paper further designs a weighted optimization framework based on Pearson correlation-based adaptive variable grouping. Experiments and analyses are conducted on three popular test suites with up to 5000 decision variables. Extensive comparisons demonstrate that the proposed Pearson correlation-based adaptive variable grouping method is superior to existing grouping methods and the weighted optimization framework based on Pearson correlation-based adaptive variable grouping outperforms state-of-the-art optimizers.
Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objectiveoptimization problem (MOP) as a whole. However, when dealing with MOPs with a large number...
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ISBN:
(纸本)9781450367486
Most multi-objective evolutionary algorithms (MOEAs) of the state of the art treat the decision variables of a multi-objectiveoptimization problem (MOP) as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases. Problem decomposition, in terms of decision variables, has been found to be extremely efficient and effective for solving largescaleoptimization problems. In this work, we study the effect of what we call "operational decomposition", which is a novel framework based on coevolutionary concepts to apply MOEAs's crossover operator without adding any extra cost. We investigate the improvements that NSGA-III can achieve when combined with operational decomposition. This new scheme is capable of improving efficiency of a MOEA when dealing with largescale MOPs having from 200 up to 1200 decision variables.
In view of the complex energy coupling and multiple market entities competition in integrated energy system, a hybrid time-scale energy optimal scheduling model based on game interaction with supply and demand is pres...
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In view of the complex energy coupling and multiple market entities competition in integrated energy system, a hybrid time-scale energy optimal scheduling model based on game interaction with supply and demand is presented. Considering the initiative of market players in the day-ahead scale, a bilateral game interaction framework for the operator of integrated energy system and a calibration model considering differences in energy distinguishing feature in the intraday scale are presented. At the intraday scale, the cold and thermal energy related equipment and controllable resources with low time resolution were modified combined with the day-ahead plan in slow layer, while the power related's with high time resolution were modified combined with the slow layer plan in fast layer. Aim at the above-mentioned complex model, such as multi-objective, non-convex, strong constraint, large-scale decision variables and irregular Pareto front shape, an adaptive reference point based largescalemulti-objective whale optimization algorithm is proposed. Finally, the bilateral interactivity and sensitivity for scheduling results were quantitatively analyzed, under the influence of power generation and consumption uncertainty. The results show that the strategy can balance the economy and robustness, improve the profit benefit of IES by 61.5% with the traditional robustness. Balance the benefits of market entities, bilateral game can increase the benefit of IES by 57.6%. The model based on different energy characteristics is more in line with the actual scheduling situation.
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