A new genetic algorithm for solving optimization problems
A new genetic algorithm for solving optimization problems
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DOI：
10.1016/j.engappai.2013.09.013
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发表时间：
2014-01-01
影响因子：
8
通讯作者：
Essam, Daryl L.
中科院分区：
文献类型：
--
作者：
Elsayed, Saber M.;Sarker, Ruhul A.;Essam, Daryl L.
在过去的二十年中，许多不同的遗传算法（GA）已被引入解决优化问题。由于在不同的优化问题的特性的变化，这些算法都没有表现出一致的性能在一系列的真实的世界的问题。任何遗传算法的成功都取决于其搜索算子的设计，以及它们的适当集成。在本文中，我们提出了一个新的多父交叉遗传算法。此外，我们提出了一个多样性算子来代替变异，并保持良好的解决方案的档案。虽然所提出的算法的目的是覆盖更广泛的问题，它可能不是所有类型的问题的最佳算法。为了评价算法的性能，我们解决了一组约束优化基准问题，以及14个著名的工程优化问题。实验分析表明，该算法收敛速度快，从而表现出上级的性能相比，其他算法，也解决了这些问题。(C)2013爱思唯尔有限公司保留所有权利。
Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems. (C) 2013 Elsevier Ltd. All rights reserved.