ABSTRACT

This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.

摘要

本文介绍了一种结合回溯搜索（BS）和差分进化（DE）算法的新颖混合技术。提出的杂交执行多样性丧失和停滞检测机制以维持种群的多样性，此外，对组成算法的变异算子进行了修改，以提高提议的搜索能力。这些修改是自适应的，并且可以同时实施。通过引入的技术，还进行了广泛的实验来建立参数的最佳配置。拟议的杂交方法已应用于5个经典版本和2个最先进的DE变体，并针对28个不同维度的著名基准函数进行了测试，每种类型都突出显示了一组不同的特征，并提供了基线测量来验证算法的性能。为了进一步测试该建议，在比较中还包括了四个最先进的算法。实验结果表明，所提出的混合框架在比较算法上是有效的。