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An Efficient Hybrid mine blast algorithm for Tackling Software Fault Prediction Problem

NEURAL PROCESSING LETTERS 2023年第8期55卷 10925-10950页

作者： Alweshah, Mohammed Kassaymeh, Sofian Alkhalaileh, Saleh Almseidin, Mohammad Altarawni, Ibrahim Al Balqa Appl Univ Prince Abdullah bin Ghazi Fac Commun & Informat Te Dept CS Salt Jordan Aqaba Univ Technol Fac Informat Technol Artificial Intelligence Dept Aqaba Jordan Aqaba Univ Technol Fac Informat Technol Software Engn Dept Aqaba Jordan Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi Malaysia Tafila Tech Univ Comp Sci Dept Tafila Jordan

An inherent problem in software engineering is that competing prediction systems have been found to produce conflicting results. Yet accurate prediction is crucial because the complexity and quality of software requirements have dramatically changed in recent years, and consumers have become considerably more demanding in terms of the cost, timeframe, and quality of software solutions. Moreover, these variables may also be in direct conflict and can only be resolved by the optimum development of software by using reliable software engineering strategies. In this paper, a novel method based on the integration of the mine blast algorithm (MBA) and the simulated annealing (SA) algorithm is used to create input connection weights and biases for a back propagation neural network (BPNN) for the purpose of addressing the software fault prediction problem (SFP). The aim of hybridizing the MBA and SA is to find a way to efficiently explore and manipulate the search space. The proposed MBA-SA was tested on 18 datasets for SFP. The results indicated that the MBA-SA outperformed the MBA on all datasets. These results were subjected to additional statistical validity, boxplot distribution, and convergence analysis. Furthermore, a comparative evaluation of MBA-SA against twenty state-of-the-art methods for various output metrics was performed, and the result indicated that the hybrid MBA-SA outperformed most other state-of-the-art methods in the majority of datasets.

关键词： Software engineering prediction problems Machine learning Backpropagation neural network mine blast algorithm Simulated annealing Hybridization metaheuristic

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A hybrid mine blast algorithm for feature selection problems

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SOFT COMPUTING 2021年第1期25卷 517-534页

作者： Alweshah, Mohammed Alkhalaileh, Saleh Albashish, Dheeb Mafarja, Majdi Bsoul, Qusay Dorgham, Osama Al Balqa Appl Univ Dept Comp Sci Salt Jordan Birzeit Univ Dept Comp Sci Birzeit Palestine German Jordanian Univ Dept Comp Sci Amman Jordan

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a better understanding of stored data. The success of the FS process depends on achieving a balance between two important factors, namely selecting the minimal number of features and maintaining the maximum accuracy in the results. In this paper, two methods are proposed to improve the FS process. Firstly, the mine blast algorithm (MBA) is introduced to optimize the FS process in the exploration phase. Secondly, the MBA is hybridized with simulated annealing as a local search in the exploitation phase to enhance the solutions located by the MBA. The proposed approaches (MBA and MBA-SA) are tested on 18 benchmark datasets from the UCI repository, and the comprehensive experimental results indicate that MBA-SA achieved good performance when compared with five approaches in the literature.

关键词： Feature selection Simulated annealing mine blast algorithm Hybrid optimization Classification

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Optimal design of fuzzy PID controller for deregulated LFC of multi-area power system via mine blast algorithm

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NEURAL COMPUTING & APPLICATIONS 2020年第9期32卷 4531-4551页

作者： Fathy, Ahmed Kassem, Ahmed M. Abdelaziz, Almoataz Y. Jouf Univ Elect Engn Dept Fac Engn Al Jouf Saudi Arabia Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt Sohag Univ Elect Engn Dept Fac Engn Sohag Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt

This paper presents a novel optimal fuzzy proportional-integral-derivative (fuzzy PID) controller for load frequency control (LFC) designed by a proposed approach of mine blast algorithm (MBA) for multi-interconnected areas. The system includes reheat thermal connected power systems with the effect of the governor dead zone and turbine generation rate constraint nonlinearity. The proposed approach is used to determine the optimal parameters of the fuzzy PID controller to minimize the integral time absolute error. The proposed controller is inserted in multi-interconnected power systems which are built in Simulink/MATLAB library;triangular membership function is used for fuzzy PID controller. Additionally, the optimum adjustment of the fuzzy PID controller parameters under contracted scenario for large step demands and disturbances is investigated by MBA. The obtained results are compared to those obtained via antlion optimizer, artificial bee colony, hybrid differential evolution particle swarm optimization and hybrid PSO pattern search algorithm. The obtained results confirmed the superiority of the proposed MBA in designing the fuzzy PID-LFC as it provides less error with best statistical parameters compared to the others.

关键词： Load frequency control Fuzzy logic control mine blast algorithm

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The mine blast algorithm for the structural optimization of electrical vehicle components

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MATERIALS TESTING 2020年第5期62卷 497-501页

作者： Yildiz, Betul Sultan Uludag Univ Dept Automot Engn Bursa Turkey

The shape optimization of mechanical and automotive component plays a crucial role in the development of auto-motive technology. Presently, the use of derivative-free metaheuristics in combination with finite element analysis for mechanical component design is one of the most focused on topics due to its simplicity and effectiveness. In this research paper, the mine blast algorithm (MBA) is used to solve the problem of shape optimization for a vehicle door hinge to prove how the MBA can be used for solving shape optimization problems in designing electrical vehicles. The results show the advantage of the MBA for designing optimal components in the automotive industry.

关键词： mine blast algorithm control arm shape optimization finite element analysis

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Self-adaptive global mine blast algorithm for numerical optimization

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NEURAL COMPUTING & APPLICATIONS 2020年第7期32卷 2423-2444页

作者： Yadav, Anupam Sadollah, Ali Yadav, Neha Kim, J. H. Dr BR Ambedkar Natl Inst Technol Dept Math Jalandhar 144011 Punjab India Univ Sci & Culture Dept Mech Engn Tehran Iran Natl Inst Technol Hamirpur Dept Math Hamirpur 177005 Himachal Prades India Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea

In this article, a self-adaptive global mine blast algorithm (GMBA) is proposed for numerical optimization. This algorithm is designed in a novel way, and a new shrapnel equation is proposed for the exploitation phase of mine blast algorithm. A theoretical study is performed, which proves the convergence of any typical shrapnel piece;a new definition for parameters values is defined based on the performed theoretical studies. The promising nature of newly designed exploitation idea is verified with the help of multiple numerical experiments. A state-of-the-art set of benchmark problems are solved with the proposed GMBA, and the optimization results are compared with seven state-of-the-art optimization algorithms. The experimental results are statistically validated by using Wilcoxon signed-rank test, and time complexity of GMBA is also calculated. It has been justified that the proposed GMBA works as a global optimizer for constrained optimization problems. As an application to the newly developed GMBA, an important data clustering problem is solved on six data clusters and the clustering results are compared with the state-of-the-art optimization algorithms. The promising results claim the proposed GMBA as a strong optimizer for data clustering application.

关键词： mine blast algorithm Global optimization Constrained optimization Data clustering

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mine blast algorithm for environmental economic load dispatch with valve loading effect

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 261-270页

作者： Ali, E. S. Abd Elazim, S. M. Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt

Economic load dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective combined economic emission dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of mine blast algorithm (MBA) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed MBA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed MBA outlasts other techniques in terms of total cost and computational time.

关键词： mine blast algorithm Economic load dispatch Combined economic emission dispatch Emission constraints Valve point loading effect

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Optimal locations and sizing of capacitors in radial distribution systems using mine blast algorithm

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ELECTRICAL ENGINEERING 2018年第1期100卷 1-9页

作者： Abd Elazim, S. M. Ali, E. S. Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt

In this paper, a mine blast algorithm (MBA) is proposed for optimal allocations and sizing of capacitors in various distribution systems. First, the most candidate buses for installing capacitors are suggested using loss sensitivity factors (LSF). Then, the proposed MBA is employed to deduce the size of capacitors and their locations from the elected buses. The objective function is designed to reduce the total cost and, consequently, to increase the net saving per year. The proposed algorithm is tested on 10 and 85 bus radial distribution systems. The obtained results via the proposed algorithm are compared with others to highlight their benefits. Moreover, the results are introduced to verify the effectiveness of the suggested algorithm to minimize the losses and total cost and to enhance the voltage profile and net saving for various distribution systems and loading conditions.

关键词： mine blast algorithm Power systems Optimal capacitor locations Loss sensitivity factors Distribution systems

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Performance Improvements of Induction Motor Drive Supplied by Hybrid Wind and Storage Generation System Based on mine blast algorithm

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ENERGIES 2019年第15期12卷 2947页

作者： Abdalla, Shiref A. Abdullah, Shahrum S. Utm Kuala Lumpur MJIIT Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

This research investigates the performance improvements of induction motor (IM) drive supplied by a hybrid wind/battery storage system based on the optimal mine blast algorithm. This is done by using IM field-oriented speed control as an independent dynamic load and a battery as a storage power unit. To ensure the accuracy and quality of energy equilibrium in autonomous wind power systems, there is an urgent and necessary demand for battery storage units. So in principle, the overall system of the complete model is configured to be combined with an uncontrolled rectifier, a steady magnetic synchronous generator (PMSG), a buck converter, and a lead-acid battery (LAB) in addition to an induction motor. According to the imposed controller, the loads required power can be obtained effectively by the prescribed battery storage and the wind generation units. Considering the suggested vector control to adjust the induction motor velocity through a three-phase inverter, it will be obtained 220 V/50 Hz, which is the principal target of this research. Moreover, the system with the proposed optimal control is compared with the optimal control based on genetic algorithm (GA) and the classical PID control. It is, therefore, possible to conclude that the results of numerical calculations and simulations illustrate that the studied system has strong achievement and perfect predictor of the electric parameter waveforms.

关键词： Index Terms-Wind turbine PMSG energy storage system IM mine blast algorithm optimization

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Performance Enhancement of Micro Grid System with SMES Storage System Based on mine blast Optimization algorithm

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ENERGIES 2019年第16期12卷 3110-3110页

作者： Alattar, Alhassan H. Selem, S. I. Metwally, Hamid M. B. Ibrahim, Ahmed Aboelsaud, Raef Tolba, Mohamed A. El-Rifaie, Ali M. Zagazig Univ Dept Elect Power & Machines Zagazig 44519 Egypt Natl Res Tomsk Polytech Univ Sch Energy & Power Engn Dept Elect Power Syst Tomsk 634050 Russia EAEA Nucl Res Ctr Cairo 11787 Egypt Moscow Power Engn Inst Elect Power Syst Dept Moscow 111250 Russia Amer Univ Middle East Coll Engn & Technol Eqaila 15453 Kuwait

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind-diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional-integral-differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind-diesel hybrid system. The proposed MBA-SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA-SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

关键词： wind turbine diesel generator SMES pitch control mine blast algorithm

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Solution of Economic Problem with Co-generation Units Using mine blast algorithm

Solution of Economic Problem with Co-generation Units Using ...

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IEEE Calcutta Conference (CALCON)

作者： Maity, Deblina Ghosh, Arkadev Banerjee, Sumit Chanda, Chandan Kumar Netaji Subhash Engn Coll EE Dept Kolkata India Dr BC Roy Engn Coll EE Dept Durgapur India Indian Inst Engn Sci & Technol EE Dept Sibpur Howrah India

ISBN: (纸本)9781538637456

In this article economic load dispatch problem with co-generation units has been solved using mine blast algorithm with some non-linearities like valve point loading effect etc. The primary objective of CHPED is to determine power and heat allocation among power only, co-generation and heat only units where cost will be minimum. This newly proposed algorithm is built on the basis of bomb explosion. This technique searches for the global optimum. It based on distance and direction of shrapnel species. This method has been tested on co-generation systems with equality and in-equality constraints and advantage of this algorithm proves by comparing with other algorithms.

关键词： mine blast algorithm valve point loading effect economic emission load dispatch transmission losses

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