Islam Sk Minhazul, Das Swagatam, Ghosh Saurav, Roy Subhrajit, Suganthan Ponnuthurai Nagaratnam
Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India.
IEEE Trans Syst Man Cybern B Cybern. 2012 Apr;42(2):482-500. doi: 10.1109/TSMCB.2011.2167966. Epub 2011 Oct 14.
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
差分进化（DE）是当前备受关注的最强大的随机实参数优化器之一。在本文中，我们提出了一种新的变异策略、一种用于DE二项式交叉的适应度诱导亲本选择方案，以及一种简单但有效的方案来调整其两个最重要的控制参数，目标是实现性能的提升。我们称为DE/current-to-gr_best/1的新变异算子是经典DE/current-to-best/1方案的一个变体。它使用从当前代中随机选择的一组（其大小为种群大小的q%）最优解来扰动亲本（目标）向量，这与DE/current-to-best/1总是选择整个种群的最优向量来扰动目标向量不同。在我们修改后的重组框架中，通过让每个变异体与当前种群中排名前p的个体之一进行常规的二项式交叉，而不是与DE所有变体中使用的具有相同索引的目标向量进行交叉，纳入了一种有偏亲本选择方案。将通过把提出的变异、交叉和参数适应策略与经典DE框架（1995年开发）相结合而得到的一个DE变体，与取自2005年IEEE进化计算大会竞赛和实参数优化特别会议的25个标准数值基准测试上的两个经典和四个最新的自适应DE变体进行比较。我们的比较研究表明，所提出的方案在很大程度上提高了DE的性能，使其在各种测试问题上能够比最新的DE变体具有统计优势。最后，我们通过实验证明，如果将我们提出的一个或多个策略与现有的强大DE变体（如jDE和JADE）相结合，它们的性能也可以得到提升。
