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Structure-Encoding Differential Evolution for Integer Programming

机译:整数编程的结构编码差分进化

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Differential Evolution is a competive method for continuous number optimization problems. A novel Structure-Encoding Differential Evolution (SEDE) algorithm was proposed for optimization problems with integer-parameter representation. In the SEDE Algorithm, each decision variable of every individual consists of two domains. One domain is float-encoding which is confined in a narrow range [0, 1]. The other domain is integer-encoding which is used to represent the problem space. A new operator, boundary-handling operator, was used to ensure each result generated by the mutation operator falling into the range [0, 1]. In addition, a new mapping operator was constructed to generate integer number from the real domain. The global convergence property of the SEDE was analyzed. The simulation results of several Benchmarks of integer programming show it is effective and efficient. Structure-encoding Differential Evolution algorithm is a new effective way for handling the integer programming problems.
机译:差分进化是解决连续数优化问题的一种竞争方法。针对整数参数表示的优化问题,提出了一种新的结构编码差分进化算法。在SEDE算法中,每个人的每个决策变量都包含两个域。一个域是浮点编码,它被限制在一个狭窄的范围[0,1]中。另一个域是整数编码,用于表示问题空间。使用新的运算符(边界处理运算符)来确保由变异运算符生成的每个结果都落在[0,1]范围内。另外,构造了新的映射运算符以从实域生成整数。分析了SEDE的全局收敛性。整数规划的几个基准的仿真结果表明它是有效的。结构编码差分进化算法是一种解决整数规划问题的新有效方法。

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