multimodal multi-objective optimization problems (MMOPs), which aim to identify as many optimal solutions as possible and exhibit multiple equivalent Pareto optimal solution sets (PSs) that correspond to the same Pare...
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multimodal multi-objective optimization problems (MMOPs), which aim to identify as many optimal solutions as possible and exhibit multiple equivalent Pareto optimal solution sets (PSs) that correspond to the same Pareto optimal front (PF), commonly arise in a wide range of optimization problems in the real world. However, some dominated solutions that exhibit greater diversity in the decision space may be substituted by non-dominated solutions with a higher level of decision space crowding. To tackle this issue, this paper proposes a multimodalmulti- objective differential evolution with series-parallel combination and dynamic neighbor strategy (MMODE_SPDN), which can balance convergence, objective space diversity and decision space diversity. Specifically, two archives are initially updated serially followed by the overall update of the parallel structure, in which the serial-first approach can enhance population diversity and the parallel structure can greatly reduce the amount of calculation. In addition, a dynamic neighbor strategy which utilizes adaptive selection among neighbors to generate difference vectors in the decision space and objective space and then adopts the main and auxiliary parent method during the mutation process is proposed. Furthermore, the utilization of an auxiliary archive and the clustering-based special crowding distance (CSCD) method are employed to facilitate the updating of the archive, thereby enhancing diversity. MMODE_SPDN is compared with other multimodal multi-objective optimization evolutionary algorithms (MMOEAs) on numerous test problems and the experimental results demonstrate that MMODE_SPDN exhibits superior performance.
Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have ...
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Increasing the accuracy and intelligence of short-term load forecasting system can improve modern power systems management and economic power generation. In recent decades, the optimized machine learning methods have been widely used in load forecasting problems because of their predictability with higher accuracy and robustness. However, most related researches only use evolutionary algorithms for parameters fine-tuning and ignore the evolutionary algorithm based decision-making support and the matching relation between the used evolutionary algorithm and machine learning method, which greatly limit the improvement of forecasting system. To dissolve the above issues, a data-driven evolutionary ensemble learning forecasting model is proposed in this paper. Firstly, a novel multimodal evolutionary algorithm based on comprehensive weighted vector angle and shift-based density estimation is proposed. Secondly, based on the proposed multimodal evolutionary algorithm, an intelligent decision-making support scheme including predictive performance evaluation, model properties analysis, structure and fusion strategy optimization, and optimal model preference selection is designed to improve the random vector functional link network based ensemble learning model and boost the forecasting accuracy. Thirdly, experimental studies on 15 test problems with up to 6000 decision variables are conducted to validate the excellent optimization ability of the proposed evolutionary algorithm. Finally, the proposed evolutionary ensemble learning method is compared with 6 other representative forecast methods on real-world short-term load forecasting datasets from Australia, Great Britain, and Norway. The experiment results verify the superiority and applicability of the proposed method.
multimodal multi-objective optimization problems aim to identify multiple equivalent Paretooptimal solution sets in the decision space, each associated with the same Pareto front in the objective space. It remains cha...
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multimodal multi-objective optimization problems aim to identify multiple equivalent Paretooptimal solution sets in the decision space, each associated with the same Pareto front in the objective space. It remains challenging to combine multimodal multi-objective optimization algorithms with appropriate machine learning techniques to balance diversity and convergence and acquire complete and evenly distributed Pareto-optimal solution sets and fronts. Therefore, we propose a particle swarm optimizer named TCAPSO, which is based on topological clustering via adaptive resonance theory. This algorithm leverages incremental learning to construct a topology, which adaptively learns from new solutions while retaining previously valuable information. The knowledge accumulated within the topology is fully applied during the algorithmic iterations, wherein a learnable solution generator and discriminator are developed. The generator advances solutions toward promising regions by employing a neighborhood construction strategy that utilizes topological node adjacency. The discriminator selects solutions by integrating historical crowding information from topological nodes in the decision space with crowding distance in the objective space, ensuring that the solutions are uniformly distributed across both spaces. In comparative experiments across 34 multimodalmulti-objective benchmark problems, TCAPSO outperformed 10 advanced multimodalmulti-objective algorithms, surpassing the closest competitors by 21.64% and 10.95% respectively in the decision and objective spaces.
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