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Titlebook: Bio-Inspired Computing: Theories and Applications; 18th International C Linqiang Pan,Yong Wang,Jianqing Lin Conference proceedings 2024 The

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978-981-97-2271-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/186346.jpg
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Gesten bei Tieren und bei Kindern,heduling tasks in a multi-cloud environment provides users with faster scheduling options. Meanwhile, there is a certain similarity between cloud scheduling tasks, and in order not to waste the similarity between tasks, similar tasks are linked together to find an optimal scheduling solution for mul
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https://doi.org/10.1007/978-3-322-90634-2ips by leveraging shared information and features, thereby improving model performance. Evolutionary transfer optimization (ETO), applied to address multi-task problems using evolutionary algorithms, incorporates the principles of transfer learning. It utilizes knowledge and experience from source t
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https://doi.org/10.1007/978-3-322-90634-2d. Therefore, the decision of economic emission dispatch (EED) problems is particularly important. In this paper, to incentivize renewable energy generation, the green certificate trading mechanism is introduced to solve from the economic level. However, as the dimensions of the EED problems are inc
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https://doi.org/10.1007/978-3-642-16361-6mance indicators in the case of considering multiple conflicting objectives, constraints and trade-offs. In order to solve the challenge of considering multiple constraints and responding to environmental changes in real time, we propose a UAV path planning method based on multi-objective dwarf mong
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Homo sapiens – vom Tier zum Halbgottng with high-dimensional data, traditional methods often exhibit lower accuracy, posing significant challenges to feature selection. In this paper, we propose a multi-objective hybrid binary balance optimizer algorithm to address this issue. This method integrates the output results of multiple filt
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Der Elementargedanke in der Medizin,ing multi-objective optimization algorithms (MOEAs) for solving SMOPs, their search granularity keeps the same for all the decision variables, which leads to significant performance deterioration when dealing with SMOPs in high-dimensional decision spaces. To tackle the issue, in this paper, a non-u
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