论文标题
实际参数单目标优化的个人重新分布的差分演变
Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization
论文作者
论文摘要
差分进化（DE）对于真实参数单目标优化非常强大。但是，在DE中仍需要开发掉入本地最佳距离时扩展或更改搜索区域的能力，以适应具有大量本地Optima的极其复杂的健身景观。我们提出了一种新的DE流，称为个人重新分布，其中当适应性进度较低时，将调用一个个体重新分配的过程。在这样的过程中，突变和交叉是标准化的，而试验向量都保持在选择中。一旦多样性超过了预定的阈值，我们的对立替换将执行，然后算法行为返回到原始模式。在基于两个基准测试套件的实验中，我们将个人在十种算法中应用。将基于个人重新分配的十种de算法的版本与原始版本进行了比较，而且还基于完全重新启动的版本，在该版本中，个人重新分配和完整重新启动基于相同的入口标准。实验结果表明，对于大多数DE算法，基于个人重新分配的版本比基于完整重新启动的原始版本和版本都更好。
Differential Evolution (DE) is quite powerful for real parameter single objective optimization. However, the ability of extending or changing search area when falling into a local optimum is still required to be developed in DE for accommodating extremely complicated fitness landscapes with a huge number of local optima. We propose a new flow of DE, termed DE with individuals redistribution, in which a process of individuals redistribution will be called when progress on fitness is low for generations. In such a process, mutation and crossover are standardized, while trial vectors are all kept in selection. Once diversity exceeds a predetermined threshold, our opposition replacement is executed, then algorithm behavior returns to original mode. In our experiments based on two benchmark test suites, we apply individuals redistribution in ten DE algorithms. Versions of the ten DE algorithms based on individuals redistribution are compared with not only original version but also version based on complete restart, where individuals redistribution and complete restart are based on the same entry criterion. Experimental results indicate that, for most of the DE algorithms, version based on individuals redistribution performs better than both original version and version based on complete restart.
