A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization.
A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization.
复制标题
一种用于全局优化的新型混合克隆选择算法，具有组合重组和改进的超突变算子
DOI：
10.1155/2016/6204728
复制
发表时间：
2016
影响因子：
--
通讯作者：
Zhang Q
中科院分区：
文献类型：
--
作者：
Zhang W;Lin J;Jing H;Zhang Q
人工免疫系统是受生物免疫系统的启发而产生的一种新的智能方法。大多数受免疫系统启发的算法都基于克隆选择原理，称为克隆选择算法（CSA）。在处理具有多峰、高维、旋转、合成等特征的复杂优化问题时，传统的CSA算法往往会出现早熟收敛和精度不高的问题。针对这些问题，本文首先提出了一种受生物组合重组启发的重组算子。重组算子通过融合随机选择的双亲信息，产生有希望的候选解，以增强CSA的搜索能力。此外，一个修改的超变异算子被引入到构造更有前途的和有效的候选解。采用一组16个常用的基准测试函数来测试重组和超变异算子的有效性和效率。与经典CSA、带重组算子的CSA（RCSA）、带重组和改进超突变算子的CSA（RHCSA）的性能比较表明，该算法显著提高了经典CSA的性能.此外，与最先进的算法比较表明，该算法是相当有竞争力的。
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.
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影响因子：
11.8
作者：
Gao, Wei-feng;Liu, San-yang;Huang, Ling-ling
通讯作者：
Huang, Ling-ling
影响因子：
8.1
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1016/j.ijepes.2014.12.083
发表时间：
2015-07-01
影响因子：
5.2
作者：
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通讯作者：
Jordehi, A. Rezaee
影响因子：
6.8
作者：
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通讯作者：
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