A Structured Differential Evolutions for Various Network Topologies
A Structured Differential Evolutions for Various Network Topologies
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发表时间：
2010
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通讯作者：
Takashi Ishimizu;K. Tagawa
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作者：
Takashi Ishimizu;K. Tagawa
- 提出了一种差分进化算法的结构化实现方法，该方法可以在不同的网络拓扑结构下并行执行。尽管包括DE在内的进化算法本质上具有并行和分布式的特性，但顺序DE（SqDE）特别适合DE的结构化实现。因此，所提出的结构化DE（StDE）是基于SqDE。通过对多种基准问题的数值实验，比较了StDE在不同网络拓扑结构下的性能与传统的不使用网络的SqDE的性能.结果表明，在许多基准问题中，StDE寻找最优解所花费的代数小于上述SqDE所花费的代数。因此，几乎的基准问题的最优解被发现更有效地通过使用拟议的StDE实现的网络拓扑结构，而不是SqDE。
— A structured implementation of Differential Evolution (DE), which can be executed in parallel by using various networks topologies, is presented in this paper. Even though Evolutionary Algorithms (EAs) including DE have a parallel and distributed nature intrinsically, Sequential DE (SqDE) is especially suited for the structured implementation of DE. Therefore, the proposed Structured DE (StDE) is based on SqDE. Through the numerical experiment conducted on a variety of benchmark problems, the performances of StDE realized on some different network topologies are compared with the conventional SqDE that uses no networks. As a result, it is shown that the number of generations spent by StDE to find optimal solutions is smaller than the number of them spent by the above SqDE in many benchmark problems. Therefore, the optimal solutions of almost of the benchmark problems are found more efficiently by using the proposed StDE realized on the network topologies rather than SqDE.