National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China.
School of Computer and Software Engineering, Xihua University, Chengdu, China.
PLoS One. 2021 Apr 30;16(4):e0250951. doi: 10.1371/journal.pone.0250951. eCollection 2021.
Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator "rand/1" of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.
差分进化 (DE) 以其简单性和高效性而受到学者的青睐，但它的探索和开发能力需要增强。本文提出了一种混合差分进化与获取共享知识算法 (GSK) 和哈里斯鹰优化 (HHO) 的算法，简称 DEGH。其主要贡献如下。首先，在 DEGH 中构建了一种混合变异算子，其中使用和改进了 GSK 的两阶段策略、DE 的经典变异算子“rand/1”和 HHO 的软围攻规则，形成了探索和开发之间平衡的双重保险机制。其次，提出了一种新的交叉概率自适应策略，以增强 DE 中变异、交叉和选择之间的内部关系。在此基础上，交叉概率和比例因子共同影响每个个体的进化，从而使所提出的算法能够更好地适应各种优化问题。此外，将所提出的 DEGH 算法与 8 种最先进的 DE 算法在 32 个基准函数上进行了比较。实验结果表明，所提出的 DEGH 算法明显优于比较算法。
