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Optimum cycle length models using atom search optimization and grasshopper optimization algorithms

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Dept Civil Engn Engn & Architecture Fac TR-71451 Kirikkale Turkey

A fixed-time traffic control system is widely used to manage the traffic flow at intersections. Cycle length has an important effect on the performance of the fixed-time control system and the Webster model is widely used in the literature to determine the cycle length. However, when the traffic flow ratio (Y) approaches 1, the Webster model loses its effectiveness and cannot determine the cycle length when Y is above 1. In the scope of this study, it is aimed to develop models that can predict cycle length for all traffic conditions in which are Y <= 1 and Y >= 1. Two different heuristic algorithms, atom search optimization (ASO) and grasshopper optimization algorithm (GOA) were used in the development of models and the performances of these algorithms were demonstrated. The efficiency of the developed models has been demonstrated by comparing them with both Webster's model and VISTRO optimization program. Models developed in exponential, power, and quadratic forms have been able to predict cycle lengths with lower delay values by showing better performance than Webster and VISTRO. In addition, statistical results show that the ASO approach is more successful than the GOA approach.

关键词： atom search optimization grasshopper optimization algorithm mathematical modeling optimum cycle length traffic engineering

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Secure relay selection scheme for Traffic Congested Zone in VANET using grasshopper optimization and modified authentication key agreement algorithms

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APPLIED INTELLIGENCE 2023年第5期53卷 5497-5518页

作者： Monisha, A. Anu Reshmi, T. R. Murugan, K. Anna Univ CEG Ramanujan Comp Ctr Chennai Tamil Nadu India Soc Elect Transact & Secur SETS Chennai Tamil Nadu India

Vehicular ad hoc network (VANET) has increased its popularity in the modern era of communication and promises to provide ubiquitous connectivity, ultra-reliable, and low-latency transmissions. In day-to-day scenario, accidents or vehicular congestion may lead to a major problem and form a Traffic Congested Zone (TCZ). To overcome this situation relay-assisted vehicular communication is used to transmit the emergency and alert information in TCZ. Selecting the relay vehicle in the dynamic vehicular environment with high node density results in difficulties as network stability and also securing the vehicle's communication remains one of the prevailing concerns in VANET applications. The proposed scheme jointly focuses on both optimal relay vehicle (RVOBU) selection and ensuring the security of RVOBU in TCZ. Initially, the location of the traffic-congested zone (TCZ), emergency/alert information, and security parameters of each vehicle are identified and stored in temporary supporting database (TS-DB) entries that are attached to each RSU. The optimal RVOBU is then selected by using the cluster-based grasshopper optimization algorithm (GOA) and securing the RVOBU using a modified AKA algorithm. Finally, an optimal and secured RVOBU is selected by verification with original and TS-DB entries. The results are compared with other optimization techniques and security is analyzed with existing work. The proposed strategy significantly forms a minimum number of a cluster with optimal RVOBU and deliberates the objective value with secured RVOBU in each iteration. As the result, the proposed scheme outperforms the existing scheme in terms of cluster lifetime, load balancing factor, verification delay, packet delivery ratio (PDR), communication overhead, and computation time.

关键词： VANET Clustering grasshopper optimization algorithm Relay vehicle AKA algorithm Security

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Evolutionary Population Dynamics and grasshopper optimization approaches for feature selection problems

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KNOWLEDGE-BASED SYSTEMS 2018年 145卷 25-45页

作者： Mafarja, Majdi Aljarah, Ibrahim Heidari, Ali Asghar Hammouri, Abdelaziz I. Faris, Hossam Al-Zoubi, Ala' M. Mirjalili, Seyedali Birzeit Univ Dept Comp Sci Birzeit Palestine Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Al Balqa Appl Univ Dept Comp Informat Syst Al Salt Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, grasshopper optimization algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. (C) 2017 Elsevier B.V. All rights reserved.

关键词： grasshopper optimization algorithm GOA Feature selection Classification Metaheuristics Evolutionary Population Dynamics optimization

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An efficient hybrid multilayer perceptron neural network with grasshopper optimization

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SOFT COMPUTING 2019年第17期23卷 7941-7958页

作者： Heidari, Ali Asghar Faris, Hossam Aljarah, Ibrahim Mirjalili, Seyedali Univ Tehran Sch Surveying & Geospatial Engn Tehran Iran Univ Jordan Business Informat Technol Dept King Abdullah II Sch Informat Technol Amman Jordan Griffith Univ Inst Integrated & Intelligent Syst Brisbane Qld 4111 Australia

This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

关键词： optimization Classification grasshopper optimization algorithm Multilayer perceptron Medical diagnosis

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The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations

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MATERIALS TESTING 2019年第8期61卷 725-733页

作者： Yildiz, Ali Riza Yildiz, Betul Sultan Sait, Sadiq M. Li, Xinyu Uludag Univ Dept Automot Engn Gorukle Bursa Turkey Univ Michigan Ann Arbor MI 48109 USA Mississippi State Univ Ctr Adv Vehicular Syst NSF & DOE Funded Res Projects Starkville MS USA KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Huazhong Univ Sci & Technol Dept Ind & Mfg Syst Engn Huazhong Peoples R China

In this research, the Harris hawks optimization algorithm (HHO), the grasshopper optimization algorithm (GOA) and the multi-verse optimization algorithm (MVO) have been used in solving manufacturing optimization problems. This paper is the first research study for the optimization of processing parameters for manufacturing processes using the HHO, the GOA, and the MVO in the literature, and in particular, for grinding operations. A well-known grinding optimization problem is solved to prove how effective the HHO, the GOA and the MVO are in solving manufacturing problems and to demonstrate superiority over other algorithms. The results of the HHO, the GOA and the MVO are compared with other methods such as the genetic algorithm, the ant colony algorithm, the scatter search, the differential evolution algorithm, the particle swarm optimization algorithm, simulated annealing, the artificial bee colony, harmony search, improved differential evolution, the hybrid particle swarm algorithm, teaching learning-based optimization algorithms, the cuckoo search, and the fractal search algorithm. The results show that the HHO, the GOA, and the MVO are efficient optimization approaches for obtaining optimal manufacturing variables in manufacturing operations.

关键词： Harris hawks optimization algorithm grasshopper optimization algorithm multi-verse optimization algorithm manufacturing grinding design

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A hybrid feed-forward neural network with grasshopper optimization for observing pattern of scour depth around bridge piers

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第22期14卷 1-11页

作者： Kaveh, Keivan Dang Nguyen Mai Quoc Bao Pham Duong Tran Anh Tech Univ Munich Inst Hydraul & Water Resources Engn Arcisstr 21 D-80333 Munich Germany Thuyloi Univ Ctr Int Educ 175 Tay Son Hanoi Vietnam Thu Dau Mot Univ Inst Appl Technol Thu Dau Mot City Binh Duong Prov Vietnam Ho Chi Minh City Univ Technol HUTECH 475A Dien Bien Phu St Ho Chi Minh City Vietnam

This paper uses the recently proposed grasshopper optimization algorithm (GOA) to develop a new hybrid, stochastic training algorithm for the feed-forward neural networks (FFNN). The state-of-the-art hybrid model is then applied to study observing pattern of scour depth which is a challenging problem in hydraulic engineering. In order to verify and control the accuracy, stability, and efficiency of the proposed model and its computational process, the model results are compared to networks trained by three different training algorithms. To achieve this, backpropagation, backpropagation with momentum, and Levenberg-Marquardt learning algorithms, which are widely used for various hydraulic problems, are chosen. The results of the model indicated that the proposed model has high stability and performance in solving regression problems. The comparison of prediction accuracy and convergence curves represented that the model could predict the maximum scour depth with the higher convergence speed. Applying the proposed model improves the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) values by approximately 6%, 30%, and 18%, respectively.

关键词： Feed-forward neural networks grasshopper optimization algorithm Pier scour

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Optimal ELM-Harris Hawks optimization and ELM-grasshopper optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting

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NATURAL RESOURCES RESEARCH 2021年第3期30卷 2647-2662页

作者： Yu, Canxin Koopialipoor, Mohammadreza Murlidhar, Bhatawdekar Ramesh Mohammed, Ahmed Salih Armaghani, Danial Jahed Mohamad, Edy Tonnizam Wang, Zengli China Railway 18Th Bur Grp 2 Engn Co Ltd Wuhan 064000 Hubei Peoples R China Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Univ Teknol Malaysia Fac Engn Ctr Trop Geoengn GEOTROPIK Sch Civil Engn Johor Baharu 81310 Malaysia Indian Inst Technol Dept Min Engn Kharagpur W Bengal India Univ Sulaimani Coll Engn Civil Engn Dept Sulaymaniyah Kurdistan Regio Iraq Nawroz Univ Sci Res & Dev Ctr Duhok Kurdistan Regio Iraq Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Most mining and tunneling projects usually require blasting operations to remove rock mass. Previous studies have mentioned that if the blasting operation is not properly designed, it may lead to several environmental issues, such as ground vibration. This study presents various machine learning (ML) techniques, i.e., hybrid extreme learning machines (ELMs) with the grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) for controlling and predicting ground vibrations resulting from mine blasting. Actually, the GOA-ELM and HHO-ELM models are improved versions of a previously developed ELM model, and they are able to provide higher performance capacity. For the proposed ML modeling, a database was established consisting of 166 datasets collected from Malaysian quarries. The efficacy of the proposed ML techniques was observed in the training stage as well as in the testing stage, and the results were evaluated against five parameters constituting the fitness criteria. The results showed that the GOA-ELM model delivered more accurate ground vibration values compared to the HHO-ELM model. The system error values of the GOA-ELM model for the training and testing datasets were 2.0239 and 2.8551, respectively. The coefficients of determination of the GOA-ELM model for the training and testing datasets were 0.9410 and 0.9105, respectively. It was concluded that the new hybrid model is able to forecast ground vibration resulting from mine blasting with high level of accuracy. The capabilities of this hybrid model can be extended further to mitigate other environmental issues caused by mine blasting.

关键词： Ground vibration Blasting environmental issues Extreme learning machine Harris hawks optimization grasshopper optimization algorithm

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Chaotic Arc Adaptive grasshopper optimization

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IEEE ACCESS 2021年 9卷 17672-17706页

作者： Wang, Guochun Heidari, Ali Asghar Wang, Mingjing Kuang, Fangjun Zhu, Wei Chen, Huiling Changchun Univ Technol Coll Appl Technol Changchun 130012 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 119077 Singapore Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://***.

关键词： optimization Particle swarm optimization Convergence Chaos Statistics Sociology Gravity grasshopper optimization algorithm swarm intelligence chaotic maps engineering design problem

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RETRACTED: An efficient grasshopper optimization with recurrent neural network controller-based induction motor to replace flywheel of the process machine (Retracted article. See APR, 2023)

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第1期43卷 151-166页

作者： Jape, Vasant M. Suryawanshi, Hiralal M. Modak, Jayant P. Govt Coll Engn Dept Elect Engn Amravati India Visvesvaraya Natl Inst Technol Dept Elect Engn Nagpur Maharashtra India Priyadarshani Coll Engn Dept Mech Engn Res & Dev Nagpur Maharashtra India

This paper proposes a convenient power electronic circuitry with a control approach for the flywheel replacement of an induction motor. The proposed control approach is the joined execution of grasshopper optimization algorithm and recurrent neural network based on duty ratio controller and hence the proposed work is named grasshopper optimization with recurrent neural network. The main contribution of this work is, the power electronic circuitry gets the input voltage samples and limits the deviation to appraise the instantaneous torque demand. The required voltage for the instantaneous torque demand is produced by the proposed control technique. In the proposed grasshopper optimization with recurrent neural network technique, the grasshopper optimization algorithm is a meta-heuristic population-based algorithm, which works from the perspective of the swarming behavior of grasshoppers in nature. In the proposed system, the recurrent neural network learning procedure is improved by the grasshopper optimization algorithm in the perspective of the minimum error objective function. the proposed grasshopper optimization with recurrent neural network technique optimizes the inverter switching states by limiting the error between the setpoint torque and the demand torque regarding objective function. With this proposed technique, the unbalance between demand torque and generated torque is found with high precision and the quicker execution to pull back out the torsional pulsation insensitive load linked transmission systems. By utilizing the proposed methodology, the extreme fluctuation of load torque due to peaky loads in an induction motor will be detected accurately. Also, the proposed technique reduces the torsional vibrations, weakness in components and minimizes the outages of uninterrupted production leading to higher profits. The proposed strategy is actualized in the MATLAB/Simulink platform and evaluated their performance. The performances are appeared different

关键词： Demand torque flywheel replacement grasshopper optimization algorithm induction motor recurrent neural network

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Frequency Control in an Autonomous Two-area Hybrid Microgrid using grasshopper optimization based Robust PID Controller 8

Frequency Control in an Autonomous Two-area Hybrid Microgrid...

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8th IEEE India International Conference on Power Electronics (IICPE)

作者： Annamraju, Anil Nandiraju, Srikanth NITW Dept EE Warangal Andhra Pradesh India

ISBN: (纸本)9781538649961

The intermittency in the output power of renewableenergy sources, along with the system and load perturbations in thestandalone multi-microgrid results in large frequency deviations. The traditional PI/PID controller is unable to provide the acceptable performance for the operating conditions as mentionedabove. To address this problem, this paper suggests an efficient loadfrequency controller for the frequency control of a microgrid (MG). In this proposed controller, the parameters of PID controllers arefine-tuned by using grasshopper optimization algorithm (GOA). Inthe proposed strategy, diesel engine generators (DEGs) and redox flow batteries (RFBs) are responsible for balancing the generation and load in the hybrid microgrid system. The proposed controller isexecuted on an autonomous 2-area hybrid microgrid test system, under various parametric and systemic disturbances. Finally, howbetter the proposed controller interms of error reduction, settlingtime and overshoot is shown by comparing with various techniques available in the literature.

关键词： frequency control robust controller two-area microgrid RFBs grasshopper optimization algorithm

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