A Hybrid Chaotic Particle Swarm Optimization with Differential Evolution for feature selection

Ajibade, Samuel-Soma M.; Ahmad, Nor Ezzaty; Zainal, Anazida

doi:10.1109/isiea49364.2020.9188198

Cited by 18 publications

(5 citation statements)

References 29 publications

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“…Consequently, it is commonly used in a number of domains as such an efficient strategy for the selection of feature" [2]. Regardless of the fact that the PSO is incredibly adept at searching for ideal components subsets, it suffers from a variety of issues, such as premature convergence, which is common in complex optimization problems [5], and they also overlook the volume of components in their search while focusing solely on reducing classification error rates. As shown in equations ( 1) and ( 2), "By resetting the flying velocity and neighborhood of each person in the swarm, the PSO method differentiates the global ideal results" [2]:…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…"Innovative, quasi, and limited systems all exhibit chaos, which is a predetermined, irregular technique. Chaos is a statistically occurring simple but spontaneous causal computational modeling structure, as well as the system that involves chaos is regarded as a source of randomness" [5]. "Chaotic maps influence the convergent rate of algorithms positively because they incorporate chaos in the realistic abreast and seem to be for only a brief duration and randomness for a predefined timeframe" [6].…”

Section: Chaotic Logistic Mapmentioning

confidence: 99%

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Utilizing Logistic Map to Enhance the Population Diversity of PSO

Ajibade

Chweya

Ogunbolu

et al. 2022

J. Phys.: Conf. Ser.

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Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: Chaotic Logistic Mapmentioning

confidence: 99%

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Ajibade

Chweya

Ogunbolu

et al. 2022

J. Phys.: Conf. Ser.

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“…Experimental studies on different datasets showed that the presented hybrid solution outperformed the current ABC strategy. The goal of this research [48] is to improve chaotic dynamic weight particle swarm optimization (CHPSO) by attempting a CHPSODE fusion of DE and CHPSO. The simulation results revealed that CHPSO-DE outperformed other strategies in finding a practical solution to the FS problem.…”

Section: ) Hybridization Of Different Evolutionary Algorithms For Fsmentioning

confidence: 99%

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

et al. 2022

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This work aims to discover the relevant factors to predict the health condition of COVID-19 patients by employing a fresh and enhanced binary multi-objective hybrid filter-wrapper chimp optimization (EBMOChOA-FW) based feature selection (FS) approach. FS is a preprocessing approach that has been highly fruitful in medical applications, as it not only reduces dimensionality but also allows us to understand the origins of an illness. Wrappers are computationally expensive but have excellent classification performance, whereas filters are recognized as quick techniques, although they are less accurate. This study presents an advanced binary multi-objective chimp optimization method based on the hybridization of filter and wrapper for the FS task using two archives. In exceptional instances, the initial ChOA version becomes stuck at the local optima. As a result, a novel ChOA termed EBMOChOA is developed here by integrating the Harris Hawk Optimization (HHO) into the original ChOA to improve the optimizer's search capabilities and broaden the usage sectors. The location change step in the ChOA optimizer is separated into three parts: modifying the population using HHO to produce an HHO-based population; creating hybrid entities according to HHO-based and ChOA-based individuals; and altering the search agent in the light of greedy technique and ChOA's tools. The effectiveness of the EBMOChOA-FW is proven by comparing it to five other well-known algorithms on nine different benchmark datasets. Then its strengths are applied to three real-world COVID-19 datasets to predict the health condition of COVID-19 patients.

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“…The installation of a temperature control system, as suggested by [16], to prevent server failure due to overheating, will eliminate scenarios where the staff walked into the data center on Monday morning after the weekend and found that the air conditioning was off due to a power loss or malfunction. The selection of feature subsets has been broadly utilized in data mining and machine learning tasks to produce a solution with a small number of features which improves the classifier's accuracy and it also aims to reduce the dataset dimensionality while still sustaining high classification performance [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Indoor Temperature and Humidity Monitoring System

Shamang

Chukwuma-Uchegbu

El-nafaty

2020

JSETM

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Monitoring Temperature and Humidity for Server Room is an IoT-based device that provides data when controlling temperature and humidity in the server room. The prototype has a variety of sensors that monitor both temperature and humidity parameters that can be calculated. This machine can be used to keep track of a room's or location's temperature and humidity. The suggested system transfers data to the cloud on a daily basis, allowing users to track data from anywhere. The device is fitted with features that dynamically include updates to consumers via the Telegram program for direct control and regulation. A Raspberry Pi and an Arduino module double as the robot brain. The Raspberry Pi is wired to a temperature and humidity sensor (DHT11). Any time these values cross the threshold set for each message sent to the user through the Telegram API. The user will remotely set the room temperature by sending an order via the telegram application based on the notification. Using telegram software or web server applications, the proposed framework efficiently tracks and dynamically manages orders.

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A Hybrid Chaotic Particle Swarm Optimization with Differential Evolution for feature selection

Cited by 18 publications

References 29 publications

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Utilizing Logistic Map to Enhance the Population Diversity of PSO

An Enhanced Binary Multiobjective Hybrid Filter-Wrapper Chimp Optimization Based Feature Selection Method for COVID-19 Patient Health Prediction

Indoor Temperature and Humidity Monitoring System

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