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CQFFA:A Chaotic Quasi-oppositional farmland fertility algorithm for Solving Engineering Optimization Problems

Journal of Bionic Engineering 2023年第1期20卷 158-183页

作者： Farhad Soleimanian Gharehchopogh Mohammad H.Nadimi-Shahraki Saeid Barshandeh Benyamin Abdollahzadeh Hoda Zamani Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad517Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad517Iran Afagh Higher Education Institute Urmia969Iran

farmland fertility algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization ***,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and *** literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these ***,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising ***,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA *** improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification *** results obtained show that the proposed CQFFA model has been significantly *** is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and ***,the CQFFA algorithm has evaluated six real-world engineering *** experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

关键词： Nature-inspired algorithm farmland fertility algorithm Chaotic maps Quasi Engineering optimization problems

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A Feature Selection Based on the farmland fertility algorithm for Improved Intrusion Detection Systems

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2022年第3期30卷 1-27页

作者： Naseri, Touraj Sattari Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

The development and expansion of the Internet and cyberspace have increased computer systems attacks;therefore, Intrusion Detection Systems (IDSs) are needed more than ever. Machine learning algorithms have recently been used as successful IDSs;however, due to the high dimensions in IDSs, Feature Selection (FS) plays an essential role in these systems' performance. In this paper, a binary version of the farmland fertility algorithm (FFA) called BFFA is presented to FS in the classification of IDSs. In the proposed method, the V-shaped function is used to move the FFA processes in the binary space, as a result of which the V-shaped function changes the continuous position of the solutions in the FFA algorithm to binary mode. A hybrid approach to classifiers and the BFFA is presented as a fast and robust IDS. The proposed method is tested on two valid IDSs datasets, namely NSL-KDD and UNSW-NB15, and is compared in Accuracy, Precision, Recall, and F1_Score criteria with K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaboost (ADA_BOOST), and Naive Bayes (NB) classifiers. The simulation results showed that the proposed method performed better than the classifiers in Accuracy, Precision, and Recall criteria;moreover, the proposed method has a better run time in the FS operation.

关键词： farmland fertility algorithm Feature selection Hybridization Intrusion detection systems

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An Improved farmland fertility algorithm for Global Function Optimization

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IEEE ACCESS 2020年 8卷 111850-111874页

作者： Wang, Yan-Jiao Chen, Ye Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

This paper proposes an improved farmland fertility (IFF) algorithm to increase the convergence rate and precision of the farmland fertility algorithm. A search mode that combines subspace and full space is proposed. The two modes are automatically converted in accordance with the current learning level of the population. Such hybrid search balances the algorithm exploration and exploitation capabilities. The global memory capacity with a fixed size is processed self-adaptively to make its size adaptively change with the iterative process. A mechanism of soil fusion based on neighbor memory learning is also proposed to expand the search range of the current population and ensure population diversity. On the basis of the cosine similarity between the population and the current optimal individual, the area to which the individual belongs is periodically redivided, and the computing resources are reasonably allocated. The results of experiments on the CEC2013 test suite indicate that IFF has evident advantages over seven excellent algorithms in terms of convergence rate and precision.

关键词： Sociology Statistics Optimization Soil Convergence Particle swarm optimization farmland fertility algorithm hybrid search self-adaptive global memory capacity neighbor memory learning population division

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A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

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APPLIED INTELLIGENCE 2021年第7期51卷 4824-4859页

作者： Hosseinalipour, Ali Gharehchopogh, Farhad Soleimanian Masdari, Mohammad Khademi, Ali Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Islamic Azad Univ Dept Psychol Sci Urmia Branch Orumiyeh Iran

Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high dimensional datasets. One of the most common feature selection methods is the wrapper method that works on the feature set to reduce the number of features while improving the accuracy of the classification. In this paper, two different wrapper feature selection approaches are proposed based on farmland fertility algorithm (FFA). Two binary versions of the FFA algorithm are proposed, denoted as BFFAS and BFFAG. The first version is based on the sigmoid function. In the second version, new operators called Binary Global Memory Update (BGMU) and Binary Local Memory Update (BLMU) and a dynamic mutation (DM) operator are used for binarization. Furthermore, the new approach (BFFAG) reduces the three parameters of the base algorithm (FFA) that are dynamically adjusted to maintain exploration and efficiency. Two proposed approaches have been compared with the basic meta-heuristic algorithms used in feature selection on 18 standard datasets. The results show better performance of the proposed approaches compared with the competing methods in terms of objective function value, the average number of selected features, and the classification accuracy. Also, the experiments on the emotion analysis dataset demonstrate the satisfactory results.

关键词： farmland fertility algorithm Feature selection Optimization Genetic operators Text psychology

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The energy management of islanded micro grid system using farmland fertility algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第2期45卷 5031-5051页

作者： Mandloi, Toshi Sharma, Shailendra Kumar Choube, S. C. Shri Govindram Seksaria Inst Technol & Sci Dept Elect Engn 23 Sir M VisvesvarayaMarg Indore 452003 Madhya Pradesh India Univ Inst Technol RGPV Dept Elect Engn Bhopal Madhya Pradesh India

An energy management in micro grid portrays a significant role in coordinating the generation with the load demand and it is characterized as an optimization problem evaluated for economic dispatch (ED). In microgrid, energy management issues have thus far primarily been addressed with the purpose of lowering the operational costs. However, for a microgrid system to manage energy effectively, the environmental problems, or issues regarding emission must be addressed. In this research work, a novel metaheuristic approach, known as farmland fertility algorithm (FFA) is proposed for obtaining ED and integrated economic emission dispatch (IEED). The different test cases for ED and IEED are considered to minimize the fuel and the emission cost respectively. For the different cases contemplated for ED and IEED, the generation cost reduces after the inclusion of RES, and also in the case when wind energy source is included alone. These results depict that the ED and IEED perform using FFA achieves a cost optimization of 9.2% and 19.51% respectively. The ED and IEED with FFA offer more cost saving and provides reduced emission in comparison to reduced gradient method (RGM), ant colony optimization (ACO), cuckoo search algorithm (CSA), interior search algorithm (ISA), Bat algorithm (BA) and directional bat algorithm (dBA).

关键词： - Energy Management System Economic dispatch farmland fertility algorithm Micro grid Distributed energy resources Distributed generator

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A farmland fertility algorithm for solving constrained engineering problems

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第17期33卷

作者： Gharehchopogh, Farhad Soleimanian Farnad, Behnam Alizadeh, Ali Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

Solving constrained engineering optimization problems is a highly significant issue, and many different approaches have been proposed in this regard. In this article, a modified farmland fertility algorithm (FFA) has been proposed. This algorithm improves new solutions by benefiting from neighborhoods produced by the new method. In the proposed algorithm, the FFA algorithm phases are modified in the update functions, and some of its variables are replaced with new ones. In the first phase of the algorithm, a mutation is also used to improve the solution of the problem with a particular rule. The results on CEC2019 standard functions were examined to determine the impact of the new parameters. Experiments were performed on 26 standard functions and two constrained engineering optimization problems. To make a better comparison, the analysis of variance, without parameters such as the Friedman test and Pairwise test, was used to evaluate and compare the algorithms. These experiments showed that the proposed algorithm is capable of high-speed convergence and can minimize most standard functions and engineering constraints within the minimum time and the least function evaluation to the optimum value. The proposed FFA could outperform the previous version of the FFA with minimum modification in convergence, CPU-time, and complexity.

关键词： constrained engineering problems farmland fertility algorithm metaheuristic optimization

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Leveraging Deep Learning and farmland fertility algorithm for Automated Rice Pest Detection and Classification Model

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第4期18卷 959-979页

作者： Hussain, A. Srikaanth, Balaji P. SRM Inst Sci & Technol Dept Networking & Commun Kattankulathur India

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor -based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning -based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADLARPDC approach classifies rice pests from rice plant images. Before processing, FFADLARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

关键词： Rice plant Pest detection Agriculture Deep learning farmland fertility algorithm

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Discrete farmland fertility optimization algorithm with metropolis acceptance criterion for traveling salesman problems

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第3期36卷 1270-1303页

作者： Benyamin, Abdollahzadeh Farhad, Soleimanian Gharehchopogh Saeid, Barshandeh Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Traveling Salesman Problem (TSP) is an intricate discrete hybrid optimization problem that is categorized as an NP-Hard problem. The objective of the TSP is to find the shortest Hamilton route between cities to visit all existing cities and returning to the original city, from which the route started. Various researches have been carried out on TSP, and manifold solutions have been proposed to find the shortest route between cities, but none has been able to solve this problem completely. In this paper, a novel discrete version of the farmland fertility algorithm is proposed, which uses three neighborhood searching mechanisms, and one Crossover operator. The Metropolis Acceptance Criterion has also been utilized to evade the local optimal traps. Furthermore, the Roulette Wheel selection technique is used to select neighboring mechanisms during the optimization process. Moreover, a local search mechanism is used to maximize the performance of the proposed algorithm. To prove the effectiveness of the contributions, and illustrate the efficiency of the proposed algorithm, the TSP library is evaluated on 37 data sets and compared with some well-known similar methods. The simulation results showed the superiority of the proposed algorithm against other comparative methods.

关键词： farmland fertility algorithm hybridization metropolis acceptance criterion optimization traveling salesman problem

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A modified farmland fertility algorithm for solving constrained engineering problems

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Concurrency and Computation: Practice and Experience 2021年第17期33卷

作者： Farhad Soleimanian Gharehchopogh Behnam Farnad Ali Alizadeh Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran

关键词： constrained engineering problems farmland fertility algorithm metaheuristic optimization

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Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第16期43卷 3633-3646页

作者： Sabo, Aliyu Abdul Wahab, Noor Izzri Othman, Mohammad Lutfi Mohd Jaffar, Mai Zurwatul Ahlam Beiranvand, Hamzeh Acikgoz, Hakan Univ Putra Malaysia UPM Adv Lightning Power & Energy Res ALPER Dept Elect & Elect Engn Fac Engn Serdang 43400 Selangor Malaysia Nigerian Def Acad Dept Elect & Elect Engn Kaduna Nigeria Univ Putra Malaysia UPM Dept Math Fac Sci Serdang Malaysia Lorestan Univ Dept Elect Engn Khorramabad Iran Gaziantep Islam Sci & Technol Univ Fac Engn & Nat Sci Dept Elect Elect Engn Gaziantep Turkey

Generally, power systems experience a variety of disturbances that can result in low frequency electromechanical oscillations. These low frequency oscillations (LFOs) take place among the rotors of synchronous generators connected to the power system. These oscillations may sustain and grow to cause system separation if no adequate damping is provided. Power system stabilizers (PSSs) are one of the alternative devices used to solve this rotor oscillation problem. The major limitation of using PSSs at the excitation system of synchronous machine is that the conventional PSS is a permanent parameter type operating under a particular system operating condition, and its parameters are acquired through trial and error. An efficient way of operating the PSS has been by designing the PSS parameters using a powerful optimization procedure. However, designing PSS damping controller is a cumbersome task as it needs a comprehensive test system modeling and a heavy computational burden on the system. In this research, a novel, model-free neuro-fuzzy controller (NFC) is designed as the LFOs' damping controller to substitute the traditional PSS system making the power system simple without complications in PSS design and parameter optimization. The proposed controller application implements the LFOs' control without a linearized mathematical model of the system, as such it makes the system less complex and bulky. Single machine infinite bus (SMIB) test system was simulated in SIMULINK domain with the PSSs and with the proposed controller to compare the NFC effectiveness. The simulation outcome for the eigenvalue study with NFC stabilizer yields steady eigenvalues that enhanced the damping status of the system greater than 0.1 with decreased overshoots and time to rise via the proposed NFC process than with the conventional FFA-PSS. Similarly, the generator transient reaction also presents the omega and delta based on the time to settle was improved by 64.66% and 28.78%, respectiv

关键词： Power system stabilizer neuro-fuzzy controller low-frequency oscillations single machine infinite bus farmland fertility algorithm

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