Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems:A Comprehensive Analysis

• 作者: Jianfu Xia Hongliang Zhang Rizeng Li Zhiyan Wang Zhennao Cai Zhiyang Gu Huiling Chen Zhifang Pan
• 来源期刊: 仿生工程学报（英文版）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: RESEARCH ARTICLES
• 页码范围: 240-256
• 页数: 17页
• 语种: 英文

摘要

The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA-KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.