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Tutorial III: Solving complex optimization problems

机译:教程三:解决复杂的优化问题

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Summary form only given. This talk provides a complete background on efficient optimization algorithms and presents in a unified view the main design questions for all families of algorithms and clearly illustrates how to solve complex optimization problems in smart grids, networks, logistics and transportation, data mining. The key search components of metaheuristics are considered as a toolbox for: Designing efficient optimization algorithms (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems: Designing efficient algorithms for multi-objective optimization problems; Solving complex optimization problems in smart grids, networks, transportation and data mining.
机译:仅提供摘要表格。该演讲提供了有关高效优化算法的完整背景,并在统一视图中提出了所有算法系列的主要设计问题,并清楚地说明了如何解决智能电网,网络,物流和运输,数据挖掘中的复杂优化问题。元启发法的关键搜索组件被认为是用于以下目的的工具箱:设计有效的优化算法(例如局部搜索,禁忌搜索,模拟退火,进化算法,粒子群优化,散布搜索,蚁群,蜂群,人工免疫系统)进行优化问题:针对多目标优化问题设计有效的算法;解决智能电网,网络,运输和数据挖掘中的复杂优化问题。

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