一种基于折射反向学习机制与自适应控制因子的改进樽海鞘群算法

为克服基本樽海鞘群算法（SSA）存在的收敛速度慢、易陷入局部最优等不足,提出了一种基于折射反向学习和自适应控制因子的新型改进樽海鞘群算法（RCSSA）.首先,采用折射反向学习机制在每一次个体的求解中计算折射反向解,极大地提高了算法收敛精度和速度.然后,将原SSA算法中引导者的自适应控制因子引入跟随者的位置更新中,有效地控制整个搜索过程并增加了算法的局部开发能力.为验证所提RCSSA算法的优化性能,采用了7个单峰、16个多峰基准测试函数以及1个工程设计问题对其进行测试.试验中,先引入两种单策略改进的SSA算法来验证所提算法的有效性,再加入鲸鱼优化算法等5个先进的智能优化算法与之进行对比,进一步验证所提算法的优越性.研究结果表明:无论对于低维度还是高维度基准优化问题,所提算法都能有效地增强原SSA算法的开发和探索能力；并且RCSSA算法在整体优化性能方面要优于其他大多数群智能算法.

A modified salp swarm algorithm based on refracted opposition-based learning mechanism and adaptive control factor

To solve the problems that the basic salp swarm algorithm (SSA) converges slowly and is easy to fall into the local optimum, a new modified SSA based on refracted opposition-based learning (ROBL) and adaptive control factor (RCSSA) was proposed. First, the ROBL mechanism was used to calculate the refracted opposite solution of individual solution, which greatly improved the convergence accuracy and speed of the algorithm. Then, the adaptive control factor of the leader in SSA was introduced into the position update of the follower, which could effectively control the entire search process and increase the local exploitation ability. To verify the optimization performance of the proposed algorithm, seven unimodal, 16 multimodal benchmark functions, and one engineering design problem were utilized to investigate the algorithm. In the experiment, two SSAs improved by single strategy were introduced to verify the proposed algorithm, and five state-of-the-art intelligent optimization algorithms such as whale optimization algorithm were added to further verify the superiority of the algorithm. Research results show that the addition of ROBL mechanism and adaptive control factor could effectively enhance the exploitation and exploration abilities of the basic SSA for both low-dimensional and high-dimensional benchmark optimization problems, and the optimization performance of RCSSA was better than most other intelligent algorithms.