scholarly journals Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

2021 ◽  
Author(s):  
Paulino José García-Nieto ◽  
Esperanza García-Gonzalo ◽  
José Pablo Paredes-Sánchez
Keyword(s):  
Critical Temperature ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Chemical Properties ◽  
Atomic Radius ◽  
Elastic Net ◽  
Multivariate Adaptive Regression Splines ◽  
Machine Learning Techniques ◽  
Humpback Whales ◽  
Whale Optimization

AbstractThis study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kun-Chou Lee ◽  
Pai-Ting Lu
Keyword(s):  
Inverse Scattering ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Hunting Behavior ◽  
Whale Optimization ◽  
The Moment

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.

scholarly journals Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2297 ◽  
Author(s):  
Wadood ◽  
Khurshaid ◽  
Farkoush ◽  
Yu ◽  
Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Operating Time ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Test Systems ◽  
Whale Optimization ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Hunting Strategy

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.

scholarly journals Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

2020 ◽  
Vol 5 (3) ◽  
pp. 147-155
Author(s):  
I-Ming Chao ◽  
Shou-Cheng Hsiung ◽  
Jenn-Long Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Position Vector ◽  
Hunting Behavior ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Solution Accuracy

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

scholarly journals Optimal Reconfiguration of Electrical Distribution System by Whale Optimization Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 2392-2398
Keyword(s):  
Optimization Algorithm ◽  
Distribution System ◽  
Electrical Power ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Voltage Profile ◽  
Electrical Power System ◽  
Electrical Distribution ◽  
Whale Optimization ◽  
Optimal Reconfiguration

The prime motto of the electrical power system is to provide the good and high quality power to the consumers. As the life in the society is expanding hugely, hence the need of the electrical power is additionally expanding suggestively. In this manner expanding the power generation as well as beating the significant issues in the electrical distribution system has turned into a test. The strange conditions can't be normal however when happened; the recuperation ought to be made as quickly as time permits. In this work, a modern artificial intelligence based algorithm is implemented for the reconfiguration of an electrical radial distribution network. This algorithm helps to bring down the active power loss and intensify the voltage profile of the network. This paper has proposed a nature-based guided metaheuristic Whale Optimization Algorithm (WOA). WOA is motivated by the smart foraging approach of the humpback whales. To ratify the efficiency of the proposed approach, WOA is successfully simulated on IEEE standard 69 bus and 119 bus system.

scholarly journals Penyelesaian Container Stowage Problem untuk Kontainer Ukuran 20 Feet menggunakan Whale Optimization Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 100
Author(s):  
Quinn Nathania PJY ◽  
Asri Bekti Pratiwi ◽  
Herry Suprajitno
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Small Data ◽  
C Programming Language ◽  
Whale Optimization ◽  
C Programming ◽  
Complex Optimization ◽  
Unloading Time

This paper has purpose to solve Container Stowage Problem (CSP) for 20 feet container using Whale Optimization Algorithm (WOA). CSP is a problem discussing about how to stowage a container on the ship where the purpose to minimize the unloading time. Moreover, 20 feet container is one of container types. WOA is a recently developed swarm-based metaheuristic algorithm that is based on the bubble net hunting maneuver technique of humpback whales for solving complex optimization problems. WOA had three procedures, first encircling prey, second bubble-net attacking method or exploitation phase, and third search for prey or exploration phase. WOA application program or resolving solve CSP for 20 feet container was made by using Borland C++ programming language which was implemented in three cases types of CSP data, first, the small data taking about nine containers with the number of  bays, rows and tiers, respectively, are 4, 4, 4. The second and third data was medium data and big data with 62 containers and 95 containers each data, and had the number of bays, rows and tiers, respectively, are 14, 4, 5. After executing the program can be concluded the unloading time will be better if the number of whales is larger, while the number of iterations and the number of parameter control for shape of a logaritma spiral  don’t affect the solution.

scholarly journals Whale Optimization Algorithm-Based DG Allotment for Loss Minimization of Distribution Networks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Power Systems ◽  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Loss Minimization ◽  
Optimum Placement ◽  
Suggested Technique ◽  
Whale Optimization

For optimum placement of distributed generation (DG) units in balanced radial distribution network for loss minimization, implementation of whale optimization algorithm (WOA), a state-of-the-art meta-heuristic optimization algorithm is proposed in this paper. Encouraged by bubble-net hunting strategy of whales, WOA mimes the collective practice of humpback whales. For validating performance in solving the mentioned problem, the suggested technique is implemented on IEEE 33-bus and IEEE 69-bus balanced radial distribution test networks. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used as a propitious substitute in practical power systems to overcome the optimum DG siting and sizing issue. Also concerning the best knowledge of the authors, it is the first report on the application of WOA in solving optimum DG siting and sizing issue.

scholarly journals Chaotic whale optimization algorithm

2018 ◽  
Vol 5 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Gaganpreet Kaur ◽  
Sankalap Arora
Keyword(s):  
Optimization Algorithm ◽  
Chaotic Maps ◽  
Heuristic Algorithms ◽  
Convergence Speed ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Global Optimality ◽  
Tent Map ◽  
Whale Optimization ◽  
Key Parameter

Abstract The Whale Optimization Algorithm (WOA) is a recently developed meta-heuristic optimization algorithm which is based on the hunting mechanism of humpback whales. Similarly to other meta-heuristic algorithms, the main problem faced by WOA is slow convergence speed. So to enhance the global convergence speed and to get better performance, this paper introduces chaos theory into WOA optimization process. Various chaotic maps are considered in the proposed chaotic WOA (CWOA) methods for tuning the main parameter of WOA which helps in controlling exploration and exploitation. The proposed CWOA methods are benchmarked on twenty well-known test functions. The results prove that the chaotic maps (especially Tent map) are able to improve the performance of WOA. Highlights Chaos has been introduced into WOA to improve its performance. Ten chaotic maps have been investigated to tune the key parameter ‘ p’ of WOA. The proposed CWOA is validated on a set of twenty benchmark functions. The proposed CWOA is validated on a set of twenty benchmark functions. Statistical results suggest that CWOA has better reliability of global optimality.

Multifactor model for the association between time-averaged serum albumin and clinical factors of hemodialysis patients based on the whale optimization algorithm (Preprint)

2020 ◽  
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Yin-Syuan Chen ◽  
Li-Yeh Chuang ◽  
Bo-Sheng Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Nutritional Status ◽  
Serum Albumin ◽  
Optimization Algorithm ◽  
Regression Models ◽  
Whale Optimization Algorithm ◽  
Superior Performance ◽  
Clinical Factors ◽  
Multifactor Model ◽  
Whale Optimization

BACKGROUND Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered a surrogate indicator of nutritional status. Whale optimization (WO)-based feature selection algorithm could address the challenges associated with the complex characteristics of multifactor interactions and could be combined with regression models. OBJECTIVE The present study aimed to demonstrate an optimal multifactor TSA-associated model, which could be applied in the interpretation of the association between TSA and clinical factors in HD patients. METHODS A total of 829 HD patients who met the inclusion criteria were analyzed. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted model, stepwise model, and whale optimization algorithm (WOA) model. RESULTS The WOA yielded the lowest Akaike Information Criterion (AIC) value, which indicated that the WOA could achieve superior performance in multifactor analysis when compared to the fully adjusted and stepwise models. The significant features in the optimal multifactor TSA-associated model included age, creatinine, potassium, and HD adequacy index (Kt/V level). CONCLUSIONS The WOA algorithm could select five features from 15 clinical factors, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction to achieve high model performance. Therefore, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients. CLINICALTRIAL All data were retrospectively collected using an approved data protocol (201800595B0) with a waiver of informed consent from patients.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

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