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

Abstract

There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This 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 challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The 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 dataset. The 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 algorithms. Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

Intrusion Detection by XGBoost Model Tuned by Improved Multi-verse Optimizer

“…NP-hard complexity with real world problems is common and hence the application of these algorithms is diverse. Some notable examples are artificial neural network optimization [7][8][9][10]12,14,15,19,21,26,32,36,48,53,54], wireless sensors networks (WSNs) [4,11,13,52,65,75], cryptocurrency trends estimations [44,49], finally the COVID-19 global epidemic-associated applications [22,25,64,66,[69][70][71]73], computer-conducted MRI classification and sickness determination [17,20,24,33,55], cloud-edge and fog computing and task scheduling [3,5,6,16,23,50,67], and lastly securing networks through intrusion detection [2,31,43,62,…”

Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

“…Consequentially, the researches experiment with various algorithms for various problems. Interesting use cases include medical diagnosis applications [15,21,25,33,42,49], wireless sensor network optimizations [5,10,13,46,57,66], stock price predictions [17], as well as intrusion detection [2,31,40,55,56,60,65] and plant classifying task [18].…”

The XGBoost Approach Tuned by TLB Metaheuristics for Fraud Detection

“…The informatics field has benefited from the all of the above algorithm types as the improvements to real-world problems can be seen in practice some of which are: medical diagnosis applications [16,22,26,36,49,58], wireless sensor network optimizations [6,11,14,55,67,77], stock price predictions [18], as well as intrusion detection [2,34,45,65,66,70,74,76] and plant classifying task [19], cloud computing scheduling, edge and fog computing [4,7,17,25,54,69], feature selection [10,21,24,35,38,56,71], dropout regularization [13], COVID-19 detection and fake news detection [27, 68,72,73,75], tuning artificial neural networks [5,8,9,12,15,20,53,57], text clustering [23], cryptocurrency price prediction as well [46], and list goes on.…”