一种用于单元制造系统设计的多目标遗传算法(外文翻译).rar
一种用于单元制造系统设计的多目标遗传算法(外文翻译),maghsud solimanpury, prem vratz and ravi shankar}*包含中文翻译和英文原文，内容详细完整，建议下载参考！中文：2400 字英文：8100 字符多目标遗传所算法的最新发展相当广泛而迅速。有很多的多目标进化算法，如那些归功于...
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一种用于单元制造系统设计的多目标遗传算法(外文翻译)
MAGHSUD SOLIMANPURy, PREM VRATz and RAVI SHANKAR}*
包含中文翻译和英文原文，内容详细完整，建议下载参考！
中文：2400 字
英文：8100 字符
多目标遗传所算法的最新发展相当广泛而迅速。有很多的多目标进化算法，如那些归功于Schaffer (1985), Hajela and Lin (1992), Horn and Nafpliotis (1993), Srinivas and Deb (1994)等的算法。不同多目标进化算法的回顾和分类可在Fonseca and Fleming (1995) and Coello (1999)中看到。这些显著点说明不同方法之间的差异是由于每个染色体的适应度函数所采用的不同策略造成的。Zitzler et al. (2000) 把不同的多目标进化算法分为三类，包括标准选择，聚类选择和Pareto选择。该算法采用标准选择策略，如Schaffer (1985)提出的向量评估遗传算法，在选择阶段目标之间的转化。在向量评估遗传算法中，出现在交配池中的种群被每个目标选择一部分。选择执行聚合的方法使用传统的多目标优化技术，而多目标被合并成一个数量的目标方程。Pareto选择为基础的算法，利用Pareto的系统定义来排列当前种群的解决方案。现有的文献中有不同的Pareto排列规则（如Goldberg 1989, Srinivas and Deb 1994）。本文中的做法属于第一类，其中选择阶段基于适应度函数。向量评估遗传算法受到批评，因为对于每个目标，它最终结果会聚集在最有方案附近(Fonseca and Fleming 1995)，这是因为，在向量评估遗传算法中，下一代交配池的每个部分的选择每次都是基于一个目标，而其他的目标则被忽略掉(Srinivas and Deb 1994)。本文中所提出的方法是基于结合所有目标的适应度方程，因此，预计该方法会克服前面所提到的局限 ......
Recent developments in multi-objective evolutionary algorithms are quite extensive and rapidly growing. There are many multi-objective evolutionary algorithms such as those due to Schaffer (1985), Hajela and Lin (1992), Horn and Nafpliotis (1993), Srinivas and Deb (1994), etc. in the literature. A review and classification of different multi-objective evolutionary algorithms can be found in Fonseca and Fleming (1995) and Coello (1999). The salient point indicating the differences between these approaches is due to the strategy by which the fitness of each chromosome is assigned. Zitzler et al. (2000) classified different multi-objective evolutionary algorithms into three categories including criterion selection, aggregation selection and Pareto selection. The algorithms employing a criterion selection strategy, e.g. the 1420 M. Solimanpur et al. vector evaluated genetic algorithm (VEGA) proposed by Schaffer (1985), switch between the objectives during the selection phase. In VEGA a certain fraction of the population appearing in the mating pool is selected with regard to each objective.The methods performing aggregation selection use conventional multi-objective optimization techniques where multiple objectives are combined into a scalar objective function. Pareto selection-based algorithms use the systematic definition of Pareto solutions to rank the solutions in the current population. There are different rules for ranking Pareto solutions in the literature (e.g. Goldberg 1989, Srinivas andDeb 1994). Our approach in this paper falls into the first category in which the selection phase is based on fitness functions. The VEGA approach is criticized because it clusters final solutions around the best solution with respect to each objective (Fonseca and Fleming 1995). This is mainly due to the fact that inVEGA each fraction of the ......
MAGHSUD SOLIMANPURy, PREM VRATz and RAVI SHANKAR}*
包含中文翻译和英文原文，内容详细完整，建议下载参考！
中文：2400 字
英文：8100 字符
多目标遗传所算法的最新发展相当广泛而迅速。有很多的多目标进化算法，如那些归功于Schaffer (1985), Hajela and Lin (1992), Horn and Nafpliotis (1993), Srinivas and Deb (1994)等的算法。不同多目标进化算法的回顾和分类可在Fonseca and Fleming (1995) and Coello (1999)中看到。这些显著点说明不同方法之间的差异是由于每个染色体的适应度函数所采用的不同策略造成的。Zitzler et al. (2000) 把不同的多目标进化算法分为三类，包括标准选择，聚类选择和Pareto选择。该算法采用标准选择策略，如Schaffer (1985)提出的向量评估遗传算法，在选择阶段目标之间的转化。在向量评估遗传算法中，出现在交配池中的种群被每个目标选择一部分。选择执行聚合的方法使用传统的多目标优化技术，而多目标被合并成一个数量的目标方程。Pareto选择为基础的算法，利用Pareto的系统定义来排列当前种群的解决方案。现有的文献中有不同的Pareto排列规则（如Goldberg 1989, Srinivas and Deb 1994）。本文中的做法属于第一类，其中选择阶段基于适应度函数。向量评估遗传算法受到批评，因为对于每个目标，它最终结果会聚集在最有方案附近(Fonseca and Fleming 1995)，这是因为，在向量评估遗传算法中，下一代交配池的每个部分的选择每次都是基于一个目标，而其他的目标则被忽略掉(Srinivas and Deb 1994)。本文中所提出的方法是基于结合所有目标的适应度方程，因此，预计该方法会克服前面所提到的局限 ......
Recent developments in multi-objective evolutionary algorithms are quite extensive and rapidly growing. There are many multi-objective evolutionary algorithms such as those due to Schaffer (1985), Hajela and Lin (1992), Horn and Nafpliotis (1993), Srinivas and Deb (1994), etc. in the literature. A review and classification of different multi-objective evolutionary algorithms can be found in Fonseca and Fleming (1995) and Coello (1999). The salient point indicating the differences between these approaches is due to the strategy by which the fitness of each chromosome is assigned. Zitzler et al. (2000) classified different multi-objective evolutionary algorithms into three categories including criterion selection, aggregation selection and Pareto selection. The algorithms employing a criterion selection strategy, e.g. the 1420 M. Solimanpur et al. vector evaluated genetic algorithm (VEGA) proposed by Schaffer (1985), switch between the objectives during the selection phase. In VEGA a certain fraction of the population appearing in the mating pool is selected with regard to each objective.The methods performing aggregation selection use conventional multi-objective optimization techniques where multiple objectives are combined into a scalar objective function. Pareto selection-based algorithms use the systematic definition of Pareto solutions to rank the solutions in the current population. There are different rules for ranking Pareto solutions in the literature (e.g. Goldberg 1989, Srinivas andDeb 1994). Our approach in this paper falls into the first category in which the selection phase is based on fitness functions. The VEGA approach is criticized because it clusters final solutions around the best solution with respect to each objective (Fonseca and Fleming 1995). This is mainly due to the fact that inVEGA each fraction of the ......
