A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

作     者：Milan Tair Nebojsa Bacanin Miodrag Zivkovic K.Venkatachalam 

作者机构：Singidunum University32 Danijelova Str.11010BelgradeSerbia Department of Applied CyberneticsFaculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：959-982页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Whale optimisation algorithm chaotic initialisation oppositionbased learning optimisation diagnostics 

摘      要：There is a growing interest in the study development of artificial intelligence and machine learning,especially regarding the support vector machine pattern classification *** study proposes an enhanced implementation of the well-known whale optimisation algorithm,which combines chaotic and opposition-based learning strategies,which is adopted for hyper-parameter optimisation and feature selection machine learning *** whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for *** Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer,diabetes,and erythemato-squamous *** performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics,including another improved whale optimisation approach,particle swarm optimisation algorithm,bacterial foraging optimisation algorithms,and genetic *** of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.