Meta-heuristic optimization reloaded

Meta-heuristic optimization reloaded
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元启发式优化重新加载

DOI:
10.1109/nabic.2011.6089650
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发表时间:
2011
期刊:
2011 Third World Congress on Nature and Biologically Inspired Computing
影响因子:
--
通讯作者:
K. Ohnishi
K. Ohnishi
中科院分区:
--
文献类型:
--
作者:
M. Köppen;Kaori Yoshida;K. Ohnishi

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我们认为元启发式优化方法分四个阶段执行(模型、最优性、算法、验证),并指出与新算法的设计相反,改变最优性阶段的潜力。因此,我们还可以将元启发式优化方法应用于不可分割或弹性商品的公平分配任务,其中最优性由(集合论)公平关系表示。作为演示,我们修复了元启发式算法(这里是强度帕累托进化算法 SPEA2 的通用版本),并提供一组 15 个公平关系,以及对关系的一般设计原则的讨论,以处理无线信道分配问题。为了进行验证,使用了与等力随机搜索的比较。该演示表明,虽然所有关系都代表相似的模型(它们都直接或间接与瓶颈流量控制算法相关),但性能差异很大。特别是,通过有序比例公平或指数有序加权平均来表示分配的公平性似乎有利于成功的元启发式搜索。
We consider the meta-heuristic approach to optimization as to be performed in four stages (model, optimality, algorithm, verification), and point out the potential of varying the optimality stage, in contrary to the design of new algorithms. Thus, we can also apply the meta-heuristic approach to optimization to the task of fair distribution of indivisible or elastic goods, where the optimality is represented by (set-theoretic) fairness relations. As a demonstration, we fix a meta-heuristic algorithm (here a generalized version of the Strength Pareto Evolutionary Algorithm SPEA2) and provide a set of 15 fairness relations, along with the discussion of general design principles for relations, to handle the Wireless Channel Allocation problem. For validation, comparison with an equal-effort random search is used. The demonstration shows that while all relations represent a similar model (they are all directly or indirectly related to the Bottleneck Flow Control algorithm), the performance varies widely. In particular, representing fairness of distribution by ordered proportional fairness or by exponential Ordered-Ordered Weighted Averaging appears to be in favour of a successfull meta-heuristic search.
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