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cuckoo optimization algorithm Based Fuzzy Logic Speed Controller for BLDC Motor

ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年第11期52卷 2065-2077页

作者： Genc, Naci Kalimbetova, Zhansaya Seidakhanovna Khoja Akhmet Yassawi Int Kazakh Turkish Univ Elect Engn Dept Turkestan Kazakhstan Yalova Univ Fac Engn Elect & Elect Engn Dept Yalova Turkiye

Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic cuckoo optimization algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic algorithm (GA), Particle Swarm optimization (PSO), Imperialist Competitive algorithm (ICA), Invasive Weed optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained.

关键词： BLDC fuzzy cuckoo optimization algorithm genetic algorithm meta-heuristics optimization algorithms

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cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

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WASTE MANAGEMENT & RESEARCH 2022年第4期40卷 458-469页

作者： Shadkam, Elham Khayyam Univ Fac Engn Dept Ind Engn POB *** Mashhad Razavi Khorasan Iran

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts - factory, consumers' and recycling centres - each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

关键词： Network design direct and reverse logistics waste management coronavirus (COVID-1) vaccine cuckoo optimization algorithm

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A Well-Structured Metaheuristic optimization Technique for Magnetic Data Inversion of 2D Dipping Dyke-like Geological Structures Using the cuckoo optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第9期50卷 6663-6672页

作者： Toushmalani, Reza Essa, Khalid S. Ibraheem, Ismael M. Islamic Azad Univ Coll Skills & Entrepreneurship Rasht Branch Rasht Iran Cairo Univ Fac Sci Geophys Dept PO 12613 Giza Egypt Univ Cologne Inst Geophys & Meteorol Pohligstr 3 D-50969 Cologne Germany

This study applies the cuckoo optimization algorithm (COA), inspired by the brood reproduction technique of cuckoo birds, to interpret magnetic anomalies of 2D dipping dyke-like structures. The primary issue addressed is the need for accurate delineation and explanation of dyke parameters, which are crucial for visualizing dyke propagation (important for volcanic hazard assessment), tracing mineralized zones associated with dykes, and understanding their geodynamic significance. Our method identifies dyke parameters at the minimum value of the suggested objective function, ensuring the best fit. The proposed COA method was tested on both noise-free numerical magnetic datasets and datasets with varying levels of random noise (5%, 10%, and 20%), as well as real-case datasets from China and the UK. A comparative analysis with particle swarm optimization (PSO) and genetic algorithm (GA) methods was conducted to evaluate the efficiency and consistency of COA. The results demonstrate that COA aligns well with existing geological and geophysical information, offering superior accuracy and robustness compared to traditional techniques. This study provides a novel and effective approach for subsurface characterization, advancing the precision of geological and geophysical interpretations.

关键词： cuckoo optimization algorithm Dipping dyke Depth estimation Mineral exploration

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OPTIMAL SYNTHESIS OF AN ADJUSTABLE SIX-BAR PATH GENERATOR MECHANISM USING A cuckoo optimization algorithm

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JOURNAL OF MECHANICS OF MATERIALS AND STRUCTURES 2022年第2期17卷 149-167页

作者： Reza, Arash Moslehi, Morteza Islamic Azad Univ Dept Mech Engn Ahvaz Branch Ahvaz Iran Politecn Torino Dept Mech & Aerosp Engn Turin Italy

This paper aims to study the optimal synthesis of an adjustable six-bar mechanism to generate a closed path that passes through target points. The proposed mechanism has been designed by the inspiration of two common linkages, crank-rocker and slider-crank mechanisms. In this research, an analytical approach is utilized to evaluate the position of members of the adjustable six-bar mechanism, and dimensional synthesis is carried out using a cuckoo optimization algorithm (COA). First, a comparison between the path generated by dimensional optimization of the primary four-bar linkage and the obtained results from works of the literature demonstrated the superior performance of COA. Afterward, having the optimal dimensions of the primary four-bar linkage, the other design variables of the adjustable six-bar linkage are achieved by reoptimizing the mechanism. The results indicate that a more accurate generated path could be obtained for the adjustable six-bar mechanism compared with the four-bar linkage.

关键词： adjustable mechanism cuckoo optimization algorithm dimensional synthesis path generator linkage six-bar mechanism

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Accident classification methodology with don't know response for PWR nuclear reactors using the cuckoo optimization algorithm and principal component analysis method

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NUCLEAR ENGINEERING AND DESIGN 2024年 423卷

作者： de Souza, Diego J. S. N. Santos, Marcelo C. Nicolau, Andressa S. Schirru, Roberto Univ Fed Rio de Janeiro Nucl Engn Program COPPE CP 68509 BR-21941972 Rio De Janeiro RJ Brazil

This study tackles the Nuclear Accident Identification Problem (NAIP) in Nuclear Power Plants (NPPs), focusing on employing the cuckoo optimization algorithm (COA). The methodology involves classifying anomalous events using data from normal operational conditions and three design basis accidents within the simulated plant state dataset of the Brazilian NPP Angra 2. The classification process is enhanced through the use of Voronoi Vectors, delineating regions of influence for each plant state and facilitating the generation of a "don't know" response. A notable feature of this approach is the integration of Principal Component Analysis (PCA) for selecting process variables, effectively reducing the dimensionality of the problem. The proposed approach achieved nearly 100 % accuracy across all classifications, even in the presence of 1 % and 2 % noise in the data.

关键词： Nuclear power plant Nuclear accidents identification cuckoo optimization algorithm Don't Know Response PCA application

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Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator

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SOFT COMPUTING 2021年第4期25卷 2911-2933页

作者： Kelidari, Mahsa Hamidzadeh, Javad Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

Feature selection, which plays an important role in high-dimensional data analysis, is drawing increasing attention recently. Finding the most relevant and important features for classifications are one of the most important tasks of data mining and machine learning, since all of the datasets have irrelevant features that affect accuracy rate and slow down the classifier. Feature selection is an optimization process, which improves the accuracy rate of data classification and reduces the number of selected features. Applying too many features both requires a large memory capacity and leads to a slow execution speed. Feature selection algorithms are often responsible to decide which features should be selected to be used during a classification algorithm. Traditional algorithms seemed to be inefficient due to the complexity of dimensions of the problem, thus evolutionary algorithms were used to improve the problem solving process. The algorithm proposed in this paper, chaotic cuckoo optimization algorithm with levy flight, disruption operator and opposition-based learning (CCOALFDO), is applied to select the optimal feature subspace for classification. It reduces the randomization in selecting features and avoids getting stuck in local optimum solutions which lead to a more interesting feature subset. Extensive experiments are conducted on 20 high-dimensional datasets to demonstrate the effectiveness and efficiency of the proposed method. The results showed the superiority of the proposed method to state-of-the-art methods in terms of classification accuracy rate. In addition, they prove the ability of the CCOALFDO in selecting the most relevant features for classification tasks. Thus, it is a reasonable solution in handling noise and avoiding serious negative impacts on the classification accuracy rate in real world datasets.

关键词： Feature selection High-dimensional data cuckoo optimization algorithm Chaotic theory Levy flight Disruption operator Opposition-based learning

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Boosting cuckoo optimization algorithm via Gaussian mixture model for optimal power flow problem in a hybrid power system with solar and wind renewable energies

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HELIYON 2024年第11期10卷 e31755页

作者： Alghamdi, Ali S. Zohdy, Mohamed A. Majmaah Univ Coll Engn Dept Elect Engn Al Majmaah 11952 Saudi Arabia Oakland Univ Elect & Comp Engn Dept Rochester MI USA

This paper presents a novel approach, the Gaussian Mixture Method-enhanced cuckoo optimization algorithm (GMMCOA), designed to optimize power flow decision parameters, with a specific focus on minimizing fuel cost, emissions, network loss, and voltage deviation. GMMCOA integrates the strengths of COA and GMM while mitigating their individual limitations. While COA offers robust search capabilities, it suffers from initial parameter dependency and the risk of getting trapped in local optima. Conversely, GMM delivers high-speed performance but requires guidance to identify the best solution. By combining these methods, GMMCOA achieves an intelligent approach characterized by reduced parameter dependence and enhanced convergence speed. The effectiveness of GMMCOA is demonstrated through extensive testing on both the IEEE 30-bus and the large-scale 118-bus test systems. Notably, for the 118-bus test system, GMMCOA achieved a minimum cost of $129,534.7529 per hour and $103,382.9225 per hour in cases with and without the consideration of renewable energies, respectively, surpassing outcomes produced by alternative algorithms. Furthermore, the proposed method is benchmarked against the CEC 2017 test functions. Comparative analysis with state-of-the-art algorithms, under consistent conditions, highlights the superior performance of GMMCOA across various optimization functions. Remarkably, GMMCOA consistently outperforms its competitors, as evidenced by simulation results and Friedman examination outcomes. With its remarkable performance across diverse functions, GMMCOA emerges as the preferred choice for solving optimization problems, emphasizing its potential for real-world applications.

关键词： Multiobjective OPF cuckoo optimization algorithm Gaussian mixture model Solar and wind energy Non-convex cost functions

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cuckoo optimization algorithm for support vector regression potential analysis: An example from the Granites-Tanami Orogen, Australia

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JOURNAL OF GEOCHEMICAL EXPLORATION 2021年 230卷 106858-106858页

作者： Roshanravan, Bijan Kreuzer, Oliver P. Mohammadi, Sadjad Bruce, Matt Davis, James Briggs, Matt Univ Birjand Fac Engn Dept Min Engn Birjand Iran Corp Geosci Grp CGSG POB 5128 Rockingham Beach WA 6969 Australia James Cook Univ Coll Sci & Engn Econ Geol Res Ctr EGRU Townsville Qld 4811 Australia Shahrood Univ Technol Fac Min Engn Petr & Geophys Shahrood Iran MDB Geoconsulting POB 464 Blackwood SA 5051 Australia Prodigy Gold NL Level 1141 Broadway Nedlands WA 6009 Australia

This study describes the supervised support vector regression method and cuckoo optimization algorithm (COA-SVR), a newly developed mineral potential modelling (MPM) technique, and a case study of its application to predicting gold potential in the Granites-Tanami Orogen (GTO), Australia. COA-SVR, which was borne out of a computer program coded in MATLAB (R), incorporates a popular radial basis function (RBF) known as the Gaussian kernel function. The COA-SVR model was trained using a series of predictor maps previously generated and described by Roshanravan et al. (2020a), and corroborated by way of a 10-fold cross-validation. The modelling results indicate that the COA-SVR approach to MPM outperformed the previous data-driven (i.e., random forest) and continuous (i.e., fuzzy gamma, geometric average, and data-driven index overlay) models of Roshanravan et al. (2020a). As such, the newly proposed COA-SVR method presents a valid approach to MPM. To further constrain the results, the gold prospective domains identified via COA-SVR and the previous models of Roshanravan et al. (2020a) were delimited, using the student's t-value and concentration-area (C-A) fractal techniques. Following the above, indices of overall performance (O-e), accuracy (A), kappa (K) and normalized density (N-d) were applied to further assess the gold prospective domains as delimited by the student's t-value and C-A fractal techniques. The results are consistent with the COA-SVR and random forest models having outperformed the more traditional modelling approaches documented by Roshanravan et al. (2020a). Given the positive results obtained in this study and similarly positive results reported by others, we believe that the apparent superior nature of machine-learning algorithms strongly merits further consideration, research and more widespread application in MPM.

关键词： Granites-Tanami Orogen Gold Mineral potential modelling cuckoo optimization algorithm Support vector regression Machine learning

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Hybrid approach based on cuckoo optimization algorithm and genetic algorithm for task scheduling

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EVOLUTIONARY INTELLIGENCE 2021年第4期14卷 1931-1947页

作者： Akbari, Mehdi Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran

One of the most important issues in designing efficient scheduling algorithms in heterogeneous distribution systems is the reduction of execution time. In the proposed algorithm, the modified operators of the cuckoo optimization algorithm and the genetic algorithm are used to achieve a relatively optimal solution with fewer repetitions of the genetic algorithm and less execution time than the cuckoo optimization algorithm. The most important innovation in the proposed algorithm is the introduction of a new operator called spiral search, which increases the variety among the samples produced in each generation. The main idea of this operator is to replace linear search with the spiral search, which allows local search between similar schedules and accelerates the achievement of a relatively optimal answer. Also the multi objective function in the proposed algorithm is used to minimize makespan and maximize parallelization. The results obtained from the proposed algorithm on a large number of standard graphs with a various range of attributes show that it is superior to the other task scheduling algorithms.

关键词： Evolutionary algorithms Task scheduling Genetic algorithm cuckoo optimization algorithm Meta-heuristic algorithms Spiral search

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Scaling up Mobile Service Selection in Edge Computing Environment with cuckoo optimization algorithm

Scaling up Mobile Service Selection in Edge Computing Enviro...

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IEEE International Conference on Services Computing (SCC)

作者： Zhu, Ming Yu, Feilong Yan, Xiukun Li, Jing Wang, Yaoting Shandong Univ Technol Sch Comp Sci & Technol Zibo Peoples R China Univ Limerick Comp Sci & Informat Syst Limerick Ireland

ISBN: (纸本)9781665416832

Nowadays, instead of providing computational capabilities in the cloud, mobile edge computing pushes computational capabilities to network edges. It is common for mobile device users to request services located on edge servers. Mobile devices are usually resource-constrained with dynamic locations of users, and an unstable mobile environment may lead to complex mobile service requirements. Therefore, dispatching requests to edge servers to maximize mobile users' satisfaction has been a crucial problem. In this paper, we solve the service selection problem in mobile edge computing and aim to enhance mobile users' satisfaction. Minimizing the overall response time is considered as a nonfunctional criterion, and we present an algorithm based on the cuckoo optimization algorithm with crossover and mutation operators. Experiments show that our approach is an effective model for selecting services in a mobile edge computing environment with better performance than the original cuckoo optimization algorithm, and some other nature-inspired algorithms.

关键词： Mobile Edge Computing cuckoo optimization algorithm QoS Service Selection

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