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An enhanced monarch butterfly optimization with self-adaptive crossover operator for unconstrained and constrained optimization problems
Natural Computing ( IF 1.7 ) Pub Date : 2020-05-21 , DOI: 10.1007/s11047-020-09794-3
Mingyang Chen

Inspired by the phenomenon of migration of monarch butterflies, Wang et al. developed a novel promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained low-dimensional optimization problems. In this paper, we firstly extend the application area of the basic MBO to solve the constrained optimization problems. At the same time, the crossover operator originally used in evolutionary algorithms (EAs) is incorporated into the butterfly adjusting operator in order to strengthen the exploitation of the basic MBO algorithm. Furthermore, the crossover rate is self-adaptively adjusted according to the fitness of the corresponding individual instead of the fixed crossover rate used in EAs. For migration operator, only individuals having better fitness are accepted and passed to the next generation instead of accepting all the individuals in the basic MBO algorithm. After incorporated all the modifications into the basic MBO algorithm, an improved MBO algorithm with self-adaptive crossover namely SACMBO, is proposed for unstrained and constrained optimization problems. Finally, the proposed SACMBO algorithm is further used to solve 22 unstrained optimization problems (with dimension of 100, 300, 500, 1000, and 1500) and 28 constrained real-parameter optimization functions from CEC 2017 competition (with dimension of 50 and 100), respectively. The experimental results indicate that the proposed SACMBO algorithm outperforms the basic MBO and other five state-of-the-art metaheuristic algorithms.



中文翻译:

具有自适应交叉算子的增强型君主蝴蝶优化算法,用于无约束和约束优化问题

王等人受到帝王蝶迁移现象的启发。开发了一种新颖的有前途的群体智能算法,称为君主蝶形优化(MBO),用于解决无约束的低维优化问题。在本文中,我们首先扩展了基本MBO的应用领域,以解决约束优化问题。同时,最初在进化算法(EA)中使用的交叉算子被合并到蝶形调整算子中,以加强对基本MBO算法的利用。此外,交叉率是根据相应个人的适应性自适应调整的,而不是EA中使用的固定交叉率。对于迁移操作员,只有具有更好适应性的个人才被接受并传递给下一代,而不是接受基本MBO算法中的所有个人。在将所有修改都并入基本MBO算法之后,针对非约束和约束优化问题,提出了一种具有自适应交叉的改进MBO算法SACMBO。最后,提出的SACMBO算法进一步用于解决CEC 2017竞赛中22个无约束的优化问题(维数分别为100、300、500、1000和1500)和28个约束的实参优化函数(维数为50和100) , 分别。实验结果表明,所提出的SACMBO算法优于基本的MBO和其他五种最新的元启发式算法。在将所有修改都并入基本MBO算法之后,针对非约束和约束优化问题,提出了一种具有自适应交叉的改进MBO算法SACMBO。最后,提出的SACMBO算法进一步用于解决CEC 2017竞赛中22个无约束的优化问题(维数分别为100、300、500、1000和1500)和28个约束的实参优化函数(维数为50和100) , 分别。实验结果表明,所提出的SACMBO算法优于基本的MBO和其他五种最新的元启发式算法。在将所有修改都并入基本MBO算法之后,针对非约束和约束优化问题,提出了一种具有自适应交叉的改进MBO算法SACMBO。最后,提出的SACMBO算法进一步用于解决CEC 2017竞赛中22个无约束的优化问题(维数分别为100、300、500、1000和1500)和28个约束的实参优化函数(维数为50和100) , 分别。实验结果表明,提出的SACMBO算法优于基本的MBO和其他五种最新的元启发式算法。提出的SACMBO算法进一步用于解决CEC 2017竞赛中22个无约束的优化问题(维度分别为100、300、500、1000和1500)和28个约束的实参优化函数(维度为50和100) 。实验结果表明,提出的SACMBO算法优于基本的MBO和其他五种最新的元启发式算法。拟议中的SACMBO算法进一步用于解决CEC 2017竞赛中22个不受约束的优化问题(维度分别为100、300、500、1000和1500)和28个受约束的实参优化函数(维度分别为50和100) 。实验结果表明,提出的SACMBO算法优于基本的MBO和其他五种最新的元启发式算法。

更新日期:2020-05-21
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