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Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems

ENERGY 2020年 210卷

作者： Javed, Muhammad Shahzad Ma, Tao Jurasz, Jakub Ahmed, Salman Mikulik, Jerzy Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China MDH Univ Sch Business Soc & Engn Vasteras Sweden AGH Univ Sci & Technol Fac Management Krakow Poland

Hybrid renewable energy systems have been widely acknowledged as a clean, affordable and reliable mechanism to generate electricity and to accomplish global sustainable development goals. In this study, first, an operating strategy and an optimization problem are developed for a hybrid, off-grid, solar-wind system based on pumped hydro battery storage, and then a non-linear optimization problem is described for the considered system. To solve the optimization problem, four different optimization techniques are employed i.e. ant colony (ACO), firefly algorithm (FA), particle swarm optimization (PSO) and genetic algorithm (GA) and their performance is compared using statistical parameters like relative error, mean absolute error and root mean square error. Each optimization technique's working principle is discussed in detail and formulated considering the proposed optimization problem. The exploration and exploitation behavior of each algorithm is comprehensively analyzed explaining that ACO and FA have higher exploitation behavior, while GA and PSO have more exploration behavior, revealing that these behavior depend on the range of operator controlling parameters, type of optimization problem and formulation structure of the optimizers. The reference controlling parameters of each optimizer (which are operator dependent) are defined for the proposed optimization problem. The results reveal that FA performs better - i.e. with the least relative error (0.126) - while PSO outperforms best in terms of least objective function value (0.2435 $/kWh). The mean efficiency of each algorithm in terms of repeated executions (30 times) is ACO = 95.94%, FA = 96.20%, GA = 93.93%, PSO = 96.20%. The proposed study could help decision-makers to choose an optimization method to solve non-linear problems in the context of storage-based, off-grid systems under different scenarios. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Hybrid pumped-hydro-battery-storage Ant colony firefly algorithm Particle swarm optimization Genetic algorithm Statistical analysis

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Developing an algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images

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REMOTE SENSING 2019年第11期11卷

作者： Pirasteh, Saied Rashidi, Pejman Rastiveis, Heidar Huang, Shengzhi Zhu, Qing Liu, Guoxiang Li, Yun Li, Jonathan Seydipour, Erfan Southwest Jiaotong Univ SWJTU Fac Geosci & Environm Engn Dept Surveying & Geoinformat Western Pk Hitech Ind Dev Zone Chengdu 611756 Sichuan Peoples R China Univ Waterloo Mobile Sensing & Data Sci Lab 200 Univ Ave Waterloo ON N2L 3G1 Canada Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran

The world has experienced urban changes rapidly, and this phenomenon encourages authors to contribute to the United Nations sustainable development goals (SDGs) 2030 and geospatial information. This study presents a proposed algorithm of change detection and extracting the borders of buildings. This proposed algorithm provides a set of instructions to describe the method of solving the problem of how extracting the boundary of buildings from the light detection and ranging (LiDAR) input data incorporating with the firefly and ant colony algorithms. The method has used two different epochs to compare buildings and to identify the type of changes in selected buildings. These changes are based on the newly built or demolished buildings. We also used drone images and mask the region-based convolutional neural network (R-CNN) method to compare the results of roof extraction of buildings vs. the proposed algorithm. This study shows that the proposed algorithm identifies the changes of all buildings with higher accuracy of extracting border of buildings than the existing methods, successfully. This study also determines that the amount of root mean square error (RMSE) is 2.40 m(2) when we use LiDAR. This proposed algorithm contributes to identifying rapidly changed buildings, and it is helpful for global geospatial information of urban management that can add best practice and solution toward the UN SDGs connectivity dilemma of urban settlement, resilience, and sustainability.

关键词： LiDAR change detection border extraction firefly algorithm ant colony algorithm drone images R-CNN

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Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第3期10卷 1110-1125页

作者： Mamoudan, Mobina Mousapour Ostadi, Ali Pourkhodabakhsh, Nima Fathollahi-Fard, Amir M. Soleimani, Faezeh Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran 111554563 Iran Univ Bamberg Fac Informat Syst & Appl Comp Sci D-96047 Bamberg Germany Islamic Azad Univ Med Sci Coll Management Sch Adv Sci & technol Tehran *** Iran Univ Victoria Peter B Gustavson Sch Business Victoria BC V8P 5C2 Canada Ball State Univ Dept Math Sci Muncie IN 47306 USA

Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.

关键词： bidirectional gated recurrent unit convolutional neural network firefly algorithm moth-flame optimization algorithm precious metals market prediction

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Competitive evolutionary algorithms for building performance database of a microwave transistor

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2018年第2期46卷 244-258页

作者： Gunes, Filiz Belen, Mehmet A. Mahouti, Peyman Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey Artvin Coruh Univ Dept Elect & Elect Engn Artvin Turkey

In this work, the simultaneous trade-off relations among the noise figure F, gain G(T), input V-in, and output V-out VSWRs of a microwave transistor operated at a certain (V-DS, I-DS, f) condition are obtained fast and as accurate as the corresponding analytical results using multiobjective optimization process without any need for expertise on the microwave device, circuit, and noise. Three powerful evolutionary algorithms, cuckoo search, firefly, and differential evolution, are implemented comparatively as a study case to obtain the trade-off relations of a typical low-noise amplifier transistor NE3511S02 for its operation between 9 and 17GHz at V-DS=2V and I-DS=10mA. Finally, differential evolution is found as the most successful algorithm to demonstrate the typical trade-off relations of NE3511S02. It can be concluded that these trade-off relations being obtained by using a signal and noise model of the transistor enable performance database covering all the (FFmin, G(T), V(in)1, V(out)1) quadruples with their (Z(S), Z(L)) termination pairs using solely an evolutionary optimization process. Thus, a small signal transistor can be identified by its performance database to be used in the design optimization of high-performance low-noise amplifiers with the full device capacity.

关键词： microwave transistor multiobjective optimization competitive evolutionary algorithms low-noise amplifier (LNA) cuckoo search algorithm firefly algorithm differential evolution algorithm

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Review of meta-heuristics and generalised evolutionary walk algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第2期3卷 77-84页

作者： Yang, Xin-She Natl Phys Lab Teddington TW11 0LW Middx England

Meta-heuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimisation problems. More than a dozen major meta-heuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrids of meta-heuristics. This paper intends to provide an overview of nature-inspired meta-heuristic algorithms, from a brief history to their applications. We try to analyse the main components of these algorithms and how and why they work. Then, we intend to provide a unified view of meta-heuristics by proposing a generalised evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.

关键词： cuckoo search CS differential evolution DE firefly algorithm FA genetic algorithm GA meta-heuristic

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A quality of service-aware routing protocol for FANETs

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷

作者： Malhotra, Atul Kaur, Sanmeet Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala Punjab India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

Flying ad hoc networks (FANETs) are a class of highly dynamic mobile ad hoc networks in which flying UAVs coordinate among themselves for delivering the data to the applications. Data routing in FANETs has been a challenge due to the inherent mobile and unpredictable movement of UAV nodes. This paper proposes a quality of service-aware routing protocol (QARP) for FANETs which incorporates hybrid nature-inspired heuristics to identify optimized routes to the destination UAVs. The routing mechanism operates in two phases. In the first phase, the neighbour discovery initializes the adapted firefly algorithm to identify nodes with least packet delivery delay to the destination. In the second phase, the Gaussian quantum-behaved particle swarm optimization meta-heuristic algorithm is utilized to identify the most optimal nodes to create a route for data delivery to destination. The proposed protocol has been tested to perform better in terms of delay, routing overhead, throughput and packet delivery ratio in comparison to MA-DP-AODV, MDA-AODV and MDRMA routing protocols. This paper proposes a quality of service-aware routing protocol (QARP) for FANETs which incorporates hybrid nature-inspired heuristics to optimize routes for data delivery. The routing mechanism operates in two phases. In the first phase, the neighbour discovery initializes the adapted firefly algorithm to identify nodes with least packet delivery delay to the destination. In the second phase, the Gaussian quantum-behaved particle swarm optimization meta-heuristic algorithm identifies the most optimal nodes to create a route for data delivery. image

关键词： delay awareness firefly algorithm flying ad hoc network routing Gaussian quantum particle swarm optimization quality of service route optimization

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Optimized Control Scheme for Frequency/Power Regulation of Microgrid for Fault Tolerant Operation

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第13期44卷 1429-1440页

作者： Abdel-Hamed, Alaa M. Ellissy, Abou El-Eyoun K. M. El-Wakeel, Amged S. Abdelaziz, Almoataz Y. El Shorouk Acad High Inst Engn Elect Power & Machines Dept Cairo Egypt El Shorouk Acad High Inst Engn Cairo Egypt Mil Tech Coll Dept Elect Power & Energy Cairo Egypt Ain Shams Univ Dept Elect Power & Machines Fac Engn Cairo 11517 Egypt

This article investigates the operation of a microgrid system through a novel control scheme. The proposed microgrid system employs various autonomous generation systems, including photovoltaic, wind, a diesel engine, a fuel cell, an aqua electrolyzer, and a battery. A simulation model for this microgrid system was developed using MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA). A proportion-integral-derivative control scheme is employed, and the parameters of proportion-integral-derivative controllers for various controllable sources are tuned with a firefly algorithm. This is done using a new proposed weighted goal attainment method for achieving improved and fault-tolerant operation. The proposed control scheme shows better performance over the classical proportion-integral-derivative and bacterial foraging-proportion-integral-derivative controller in both transient and steady-state conditions. The firefly algorithm-proportion-integral-derivative controller also shows stronger robustness properties against system perturbations, disturbances, and faults than that with other controller structures. The robustness is a highly desirable property in such a scenario since many components of the microgrid may be switched on/off or may run at lower/higher power output at different time instants.

关键词： microgrid proportion-integral-derivative control firefly algorithm bacterial foraging optimization generation rate constraint photovoltaic diesel engine weighted goal attainment method distributed generation

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Optimal test sequence generation in state based testing using moth flame optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第5期35卷 5203-5215页

作者： Sharma, Rashmi Saha, Anju GGSIPU USICT New Delhi India

Software testing contributes a strategic role in software development, as it underrates the cost of software development. Software testing can be categorized as: testing via code or white box testing, testing via specification or black box and testing via UML models. To minimize the issues associated with object-oriented software testing, testing via UML models is used. It is a procedure which derives test paths from a Unified Modelling Language (UML) model which describes the functional aspects of Software Under Test (SUT). Thus, test cases have been produced in the design phase itself, which then reduces the corresponding cost and effort of software development. This early discovery of faults makes the life of software developer much easier. Also, there is a strong need to optimize the generated test cases. The main goal of optimization is to spawn reduced and unique test cases. To accomplish the same, in this research, a nature-inspired meta-heuristic, Moth Flame Optimization algorithm has been offered for model based testing of software based on object orientation. Also, the generated test cases have been compared with already explored meta-heuristics, namely, firefly algorithm and Ant Colony Optimization algorithm. The outcomes infer that for large object-oriented software application, Moth Flame Optimization algorithm creates optimized test cases as equated to other algorithms.

关键词： Ant Colony Optimization cuckoo search algorithm firefly algorithm genetic algorithm moth flame algorithm meta-heuristics object-oriented state transition diagram

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Fast Linde-Buzo-Gray (FLBG) algorithm for Image Compression through Rescaling Using Bilinear Interpolation

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JOURNAL OF IMAGING 2024年第5期10卷 124-124页

作者： Bilal, Muhammmad Ullah, Zahid Mujahid, Omer Fouzder, Tama Univ Technol Dept Informat Engn Technol Nowshera 24170 Pakistan Pak Austria Fachhochschule: Inst Appl Sci & Techno Dept Elect & Comp Engn Mang Haripur Haripur 22621 Pakistan Univ Girona Inst Informat & Aplicac Girona 17003 Spain Daffodil Int Univ Dept Elect & Elect Engn Dhaka 1216 Bangladesh

Vector quantization (VQ) is a block coding method that is famous for its high compression ratio and simple encoder and decoder implementation. Linde-Buzo-Gray (LBG) is a renowned technique for VQ that uses a clustering-based approach for finding the optimum codebook. Numerous algorithms, such as Particle Swarm Optimization (PSO), the Cuckoo search algorithm (CS), bat algorithm, and firefly algorithm (FA), are used for codebook design. These algorithms are primarily focused on improving the image quality in terms of the PSNR and SSIM but use exhaustive searching to find the optimum codebook, which causes the computational time to be very high. In our study, our algorithm enhances LBG by minimizing the computational complexity by reducing the total number of comparisons among the codebook and training vectors using a match function. The input image is taken as a training vector at the encoder side, which is initialized with the random selection of the vectors from the input image. Rescaling using bilinear interpolation through the nearest neighborhood method is performed to reduce the comparison of the codebook with the training vector. The compressed image is first downsized by the encoder, which is then upscaled at the decoder side during decompression. Based on the results, it is demonstrated that the proposed method reduces the computational complexity by 50.2% compared to LBG and above 97% compared to the other LBG-based algorithms. Moreover, a 20% reduction in the memory size is also obtained, with no significant loss in the image quality compared to the LBG algorithm.

关键词： computational time codebook firefly algorithm bat algorithm image compression Linde-Buzo-Gray peak signal to noise ratio vector quantization

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Data driven surrogate model-based optimization of the process parameters in electric discharge machining of D2 steel using Cu-SiC composite tool for the machined surface roughness and the tool wear

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REVISTA DE METALURGIA 2023年第2期59卷

作者： Somani, Nalin Walia, Arminder Singh Gupta, Nitin Kumar Panda, Jyoti Prakash Das, Anshuman Das, Sudhansu Ranjan DIT Univ Dept Mech Engn Dehra Dun India Thapar Polytech Coll Patiala Punjab India VSSUT Burla Dept Prod Engn Burla Odisha India

Electrical discharge machining (EDM) is mainly utilized for the die manufacturing and also used to machine the hard materials. Pure Copper, Copper based alloys, brass, graphite, steel are the conventional electrode materials for EDM process. While machining with the conventional electrode materials, tool wear becomes the main bottleneck which led to increased machining cost. In the present work, the composite tool tip comprises 80% Copper and 20% silicon carbide was used for the machining of hardened D2 steel. The powder metallurgy route was used to fabricate the composite tool tip. Electrode wear rate and surface roughness were assessed with respect to the different process parameters like input current, gap voltage, pulse on time, pulse off time and dielectric flushing pressure. During the analysis it was found that Input current (Ip), Pulse on time (Ton) and Pulse off time (Toff) were the significant parameters which were affecting the tool wear rate (TWR) while the Ip, Ton and flushing pressure affected more the surface roughness (SR). SEM micrograph reveals that increase in Ip were used to develop machine learning based surrogate models. Three machine learning (ML) models are random forest, polynomial regression and gradient boosted tree. The predictive capability of ML based surrogate models was assessed by contrasting the R2 and mean square error (MSE) of prediction of responses. The best surrogate model was used to develop a complex objective function for use in firefly algorithm-based optimization of input machining parameters for minimization of the output responses. leads to increase in the wear rate of the tool. The data obtained from experiments

关键词： Data driven modeling Electric discharge machining firefly algorithm Machine learning Surface roughness Tool wear

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