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An effective hybrid harmony search and cuckoo optimization algorithm based fuzzy PID controller for load frequency control

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APPLIED SOFT COMPUTING 2018年 65卷 121-138页

作者： Gheisarnejad, Meysam Islamic Azad Univ Najafabad Branch Dept Elect Engn Esfahan Iran

In this paper, a hybrid algorithm is developed by incorporating the egg-laying and immigration mechanisms of cuckoo optimization algorithm (COA) into harmony search (HS) algorithm (HSCOA), to design a secondary controller for two practical models of load frequency control (LFC) problem. Initially, a two-area non-reheat thermal power system is considered and the gains of PID and fuzzy PI/PID controllers are adjusted by the proposed tuning method. The superiority of HSCOA in regulating controller gains is demonstrated by evaluation and comparison of the obtained transient outcomes over some other published approaches in literature. To prove the satisfaction of the robustness in designed LFC by means of the proposed method, the performance of HSCOA based fuzzy PID controller is extensively verified under varying loading condition and some critical parameters related to the considered power plant. To add further practical challenge, the governor dead band (GDB) is included in the concerned system modeling to study the advantages of the HSCOA tuned fuzzy PID controller in handling the properties of nonlinearity in the system model. Time domain simulation of transient responses indicates that the designed controller operates satisfactorily to deal with the GDB nonlinearity and outperform other published techniques. Furthermore, to demonstrate the effective feasibility of the proposed method, the study is extended to a two-area multi-source power system with/without consideration of HVDC link. It is observed that HSCOA optimized fuzzy PID controller gives superior quality outcomes in comparison to other reported strategies. Finally, the robustness of the controllers gains designed for the concerned power system is investigated under various scenarios of change in size, location and pattern of step load perturbation. (c) 2018 Elsevier B.V. All rights reserved.

关键词： Harmony search algorithm cuckoo optimization algorithm Fuzzy logic controller Load frequency control Governor dead band

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Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2015年 22卷 35-41页

作者： Behnasr, Masoud Jazayeri-Rad, Hooshang Petr Univ Technol Dept Instrumentat & Automat Engn Ahvaz Iran

In process industries, use of the data-driven soft sensors for the purpose of process control and monitoring has gained much popularity. Data-driven soft sensors infer the process quality variables from the available historical process data. A considerable amount of process data such as pressures, temperatures, etc., are measured routinely and stored permanently. However, the quality of these data often varies. Measurement noises and data outliers are the most common effects which lead to poor quality of process data. Application of standard statistical techniques to operate data may lead to model deterioration due to contaminating observations. Therefore, the objective of this paper is to present a robust approach for the development of data-driven soft sensors. In this paper, the modeling method that is used to develop soft sensor is a combination of Nonlinear Auto Regressive with exogenous inputs (NARX) structure with Least Squares Support Vector Regression (LSSVR). The LSSVRs' parameters are optimized by a new evolutionary optimization technique known as the cuckoo optimization algorithm (COA). Then in order to make the soft sensor robust against the data outliers and noises especially the long tail noises, a new approach is proposed. The proposed method is based on the Iteratively Weighted LSSVR (IWLSSVR) which uses the Myriad weighting function. The proposed approach was applied to the prediction of the n-butane (C4) concentration in a debutanizer column unit. The technique was consequently compared against the conventional LSSVR algorithm which is based on the quadratic loss function. It turns out that reweighting the LSSVR estimate using the Myriad weight function improves the performance of the LSSVR-based soft sensor when noises and outliers exist in the measured data. The designed robust soft sensor is also compared with another robust soft sensor which is recently developed based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by the Parti

关键词： Robust soft sensor Least squares support vector regression Data outlier Myriad weight function cuckoo optimization algorithm ANFIS

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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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Filter-Based Multi-Objective Feature Selection Using NSGA III and cuckoo optimization algorithm

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IEEE ACCESS 2020年 8卷 76333-76356页

作者： Usman, Ali Muhammad Yusof, Umi Kalsom Naim, Syibrah Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Malaysia Fed Coll Educ Tech Dept Comp Sci Gombe 760212 Nigeria Woosong Univ Endicott Coll Int Studies ECIS Technol Dept Daejeon 300718 South Korea

Feature selection aims to confiscate inappropriate features and yet improve classification performance. These aims are conflicting with one another, and a choice must be made in the presence of the trade-off between them. Numerous researches deal with feature selection problem but, they are mostly single-objective based. Nowadays, multi-objective optimisation approaches are becoming the most suitable approaches to deal with feature selection problems. They can easily create a balance between selected features and classification accuracy or error rate. Evolutionary computation techniques have been applied for multi-objective feature selection. cuckoo optimisation algorithm is among the most popular technique that is exceptional in solving the problems of feature selection. Based on the binary cuckoo optimisation algorithm, two different multi-objective filter-based feature selection frameworks are presented with the idea of nondominated sorting genetic algorithms NSGAIII (BCNSG3) along with NSGAII (BCNSG2). Thus, four multi-objective filter-based feature selection approaches are proposed by employing mutual information along with gain ratio based-entropy as the respective filter evaluation measures in all the proposed frameworks. The results obtained are examined and analysed against the existing methods and single objective scheme on fourteen (14) datasets of varying degree of difficulties. The outcome of the experiments displays that the proposed multi-objective algorithms successfully derive a set of nondominated solutions that used the least feature size and attained the best error rate than using full-length features. In general, BCNSG2 obtained the best results compared to the existing methods and single-objective algorithm, whereas BCNSG3 outdoes all other approaches.

关键词： Feature extraction optimization Redundancy Genetic communication Error analysis Mutual information cuckoo optimization algorithm multi-objective feature selection NSGA III NSGA II gain ratio based-entropy mutual information machine learning and classification

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Using cuckoo optimization algorithm and imperialist competitive algorithm to solve inverse kinematics problem for numerical control of robotic manipulators

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2015年第5期229卷 375-387页

作者： Bayati, Mostafa Semnan Univ Dept Elect & Comp Engn Semnan Iran

Inverse kinematics is one of the most important and complicated problems in robotics, and there is almost no exact analytical solution for this problem. Alternatively, with significant growth in machine learning techniques in recent decades, numerical methods are widely being used to solve this problem. This article aims to present a novel application of two powerful meta-heuristic optimization algorithms including cuckoo optimization algorithm and imperialist competitive algorithm to solve robotic manipulators' inverse kinematics problem for the first time. Recently, these two algorithms have been used to solve several problems in different majors more efficiently in comparison with other well-known algorithms. To validate the efficiency of proposed approaches and suggest them as the preferred numerical methods to solve this problem, a comprehensive study has been done on performance of almost all recently used numerical methods to solve the same problem including genetic algorithm, particle swarm optimization, harmony search and differential evolution algorithms as well as adaptive neuro-fuzzy inference system and two artificial neural networks (multilayer perceptron and radial basis function). Simulations have been performed on an anthropomorphic arm with spherical wrist manipulator case study because of its complex model and degree of freedom. Proposed approach can be included in computer-aided manufacturing packages to increase robotic manipulators' efficiencies.

关键词： Robotic manipulator inverse kinematics machine learning cuckoo optimization algorithm imperialist competitive algorithm numerical control

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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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cuckoo optimization algorithm Based Fuzzy Logic Speed Controller for BLDC Motor

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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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A discrete cuckoo optimization algorithm for consolidation computing

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 115卷 495-511页

作者： Tavana, Madjid Shandi-Pashaki, Saleh Teymourian, Ehsan Santos-Arteaga, Francisco J. Komaki, Mohammad La Salle Univ Business Syst & Analyt Dept Distinguished Chair Business Analyt Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Shomal Univ Dept Ind Engn Fac Engn Amol Iran Rutgers State Univ Dept Management Sci & Informat Syst New Brunswick NJ USA Rutgers State Univ Dept Management Sci & Informat Syst Newark NJ USA Free Univ Bolzano Fac Econ & Management Bolzano Italy Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

Consolidation problems in cloud computing (CC) encompass server consolidation, virtual machine (VM) consolidation, and task consolidation. These problems have become increasingly challenging for resource allocation in distributed systems. Group technology (GT) has been effectively used to manage resource allocation problems by reducing manufacturing costs and increasing system productivity. We propose a discrete cuckoo optimization algorithm (DCOA) based on GT for consolidation in CC. The proposed model is designed to control manufacturing costs (i.e., energy, penalty, VM creating, and task migration). The DCOA developed in this study contains several new adjustments that allow it to solve large-sized discrete problems, including a grouping strategy based on the Jaccard similarity coefficient, as well as modified egg laying and immigration processes. A numerical example is used to demonstrate the applicability of the proposed model and exhibit the efficacy of the DCOA. The results illustrate the quality superiority of the DCOA over the first fit (FF) and round robin (RR) algorithms, and the efficiency and effectiveness superiority of the DCOA over the genetic algorithm (GA).

关键词： Cloud computing Group technology Mathematical model Virtual machine cuckoo optimization algorithm

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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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Group technology-based model and cuckoo optimization algorithm for resource allocation in cloud computing 15

Group technology-based model and cuckoo optimization algorit...

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15th IFAC Symposium on Information Control Problems in Manufacturing

作者： Shandi-Pashaki, S. Teymourian, Ehsan Kayvanfar, Vahid Komaki, G. H. M. Sajadi, A. Shomal Univ Fac Engn Dept Ind Engn Amol Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA

The control of operational costs is one of the main goals of resource management problem in cloud computing (CC). This paper presents a new mathematical model based on group technology (GT) to map the virtual machines (VMs) to workflows in order to control sonic costs (e.g. transfer costs, penalty costs and server cost) when the VMs are running. GT is a well-known manufacturing technique in industrial environments which can control some measures (e.g. part movements, resource utilization). In large size problems a cuckoo optimization algorithm (COA) is proposed. To test the effectiveness of our approaches, we first generate a set of problems randomly and then compare the model and COA with a well-known algorithm in literature called Round robin (RR). Analyzing the computational results proves that our approaches give better performance than RR. (C) 2015, IFAC (International federation of Automatic Control) Hosting by Elsevier Lid All rights reserved.

关键词： Cloud computing Group technology Virtual machine cuckoo optimization algorithm

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