基于统计引导和多项式差分学习的樽海鞘优化算法

Salp Swarm Algorithm Based on Statistical Guidance and Polynomial Difference Learning

樽海鞘优化算法相较于传统的群体智能优化算法,具有较好的鲁棒性和寻优能力。但仍存在全局寻优能力有限、执行效率不够高、易陷入局部极值的缺陷。针对上述问题,本文提出一种新的多项式差分学习策略,以区分和改进传统的线性差分方法;并设计一种随机种群划分方式,使得信息可以在邻域拓扑内均匀传递;另外,本文定义多项式差分学习的全局探索算子和局部开发算子,引入统计引导系数A,开启不同的多项式学习方法,从而进一步提高算法的全局搜索能力和寻优精度。最后,本文通过标准测试函数和实际应用问题的对比检验,证实了改进算法的优越性和鲁棒性,拓展和丰富了原算法的应用范围。

The Salp Swarm Algorithm(SSA)has better robustness and optimization capacity than the traditional swarm intelligence optimization algorithm.However,there are still some defects such as the limited global optimization ability,low execution efficiency and easily running into the local extreme value.In allusion to these problems,this paper proposes a new method of polynomial differential learning strategy to distinguish and improve the traditional linear difference method,and also designs a random population partition method to make the information transfer uniformly in the neighborhood topology.In addition,we define the global exploration operator and local development operator of polynomial difference learning,and introduce the statistical guiding coefficient A to develop different polynomial learning methods,thereby further improving the global search ability and optimization precision of the algorithm.Finally,the numerical comparison experiments and practical problem tests are made to confirm the superiority and robustness of the improved algorithm,and expands the application range of the original algorithm.