ETCH
发表于 2025-3-30 09:39:45
Book 2010orks and fuzzy logic. reduction of expensive evaluations (modelling, variable-fidelity, fitness inheritance), frameworks for optimization (model management, complexity control, model selection), parallelization of algorithms (implementation issues on clusters, grids, parallel machines), incorporatio
烦忧
发表于 2025-3-30 16:02:18
Strategie und Technik des Automobilmarketingutionary hybrid algorithms. In this chapter, we present the most successful implementations of such algorithms and discuss such implementations based on our experience in the development of industrial applications for planning and operation of electric power distribution networks for a period of over ten years.
尖
发表于 2025-3-30 17:17:53
Large-Scale Network Optimization with Evolutionary Hybrid Algorithms: Ten Years’ Experience with theutionary hybrid algorithms. In this chapter, we present the most successful implementations of such algorithms and discuss such implementations based on our experience in the development of industrial applications for planning and operation of electric power distribution networks for a period of over ten years.
Vasodilation
发表于 2025-3-30 22:49:57
Book 2010ions reduces costs and shortens development times but introduces new challenges to design optimization process. Examples of such challenges include limited computational resource for simulation runs, complicated response surface of the simulation inputs-outputs, and etc...Under such difficulties, cl
Crumple
发表于 2025-3-31 01:53:15
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membrane
发表于 2025-3-31 09:03:21
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翻动
发表于 2025-3-31 10:25:46
Wachstumsstrategien in neuen Märktensearch trajectories, which act competitively and/or cooperatively. This can be accomplished using parallel processing. Thus, in this paper we propose a hybrid parallel implementation for the GRASP metaheuristics and the genetic al gorithm, using reinforcement learning, applied to the symmetric traveling salesman problem.
LEER
发表于 2025-3-31 15:49:25
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exhilaration
发表于 2025-3-31 20:48:17
Improving Local Convergence in Particle Swarms by Fitness Approximation Using Regression multiple local peaks. The combination of this technique and a speciation-based PSO compares favourably to another multi-swarm PSO algorithm that has proven to be working well on the Moving peaks test functions.