基于Logistic混沌映射优化的君主蝶优化算法

君主蝶优化算法(Monarch Butterfly Optimization, MBO)是2015年提出的一种模拟君主蝶的迁徙行为的元启发式算法. 通过对MBO的研究发现其在处理高维问题时易陷入局部最优与迁移算子产生的子代受父代影响过大的问题, 本文提出新算法, Logistic混沌映射君主蝶优化算法(Monarch ...

Monarchs Butterfly Optimization Algorithm Based on Logistic Chaotic Map Optimization

Monarch Butterfly Optimization (MBO) is a meta-heuristic algorithm proposed in 2015 to simulate the migration behavior of monarch butterflies. There are two problems in the MBO algorithm when dealing with high-dimensional problems: It is easy to fall into local optimum, and the offspring generated by migration operators are greatly influenced by their parents. For these reasons, we propose a new algorithm, Monarch Butterfly Optimization with a Logistic Chaotic Map (LCMMBO). It uses a Logistic chaotic map to disturb the optimal solution and optimizes the offspring transfer mode in the migration operators. These operations respectively aim to enhance the ability to jump out of the local optimum and the ability of global search. The simulation results show that, in the case of handling high-dimensional optimization problems, the new algorithm enjoys excellent robustness and a strong ability to jump out of the local optimum.