Handedness
发表于 2025-3-26 22:41:01
https://doi.org/10.1007/978-3-540-29807-6luated by comparing it with dynamic multi-objective evolutionary algorithms using six benchmarks. The effectiveness of our algorithm is demonstrated in the experimental study where it outperforms other compared algorithms in most of the tested instances considered.
蜿蜒而流
发表于 2025-3-27 02:54:28
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Scintigraphy
发表于 2025-3-27 09:04:13
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indoctrinate
发表于 2025-3-27 12:21:59
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Cleave
发表于 2025-3-27 16:50:42
Extending Boundary Updating Approach for Constrained Multi-objective Optimization Problems an evaluation of the BU method was conducted by comparing its performance to an approach without the BU method while the feasibility rules (as an explicit CHT) work alone. The results show that the proposed method can significantly boost the solutions of constrained multi-objective optimization.
–DOX
发表于 2025-3-27 17:48:46
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pellagra
发表于 2025-3-28 00:53:31
A New Prediction-Based Algorithm for Dynamic Multi-objective Optimization Problemsluated by comparing it with dynamic multi-objective evolutionary algorithms using six benchmarks. The effectiveness of our algorithm is demonstrated in the experimental study where it outperforms other compared algorithms in most of the tested instances considered.
镇痛剂
发表于 2025-3-28 04:15:45
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rheumatism
发表于 2025-3-28 06:50:03
An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicleses an indirect encoding and a problem-specific crossover operator. Furthermore, we propose the use of a surrogate fitness function. Experimental results on problem instances with up to 100 vehicles and 1600 reservations show that the proposed approach is able to notably outperform two approaches based on mixed integer linear programming.
一瞥
发表于 2025-3-28 13:36:31
Multi-agent vs Classic System of an Electricity Mix Production Optimizationtion. It takes into account technological, economic, and environmental constraints. Evolutionary algorithms (DE, (1+1)-ES and NgOpt) from the Nevergrad library are used to generate solutions. The results show that the multi-agent system based method outperforms the classic one thanks to its ability to react to events and provide dynamic schedule.