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

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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Prediction and minimization of blast-induced flyrock using gene expression programming and cuckoo optimization algorithm

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ENVIRONMENTAL EARTH SCIENCES 2021年第1期80卷 12-12页

作者： Dehghani, Hesam Pourzafar, Mehran zadeh, Mostafa Asadi Hamedan Univ Technol Min Engn Fac Hamadan Hamadan Iran

Flyrock is one of the most dangerous adverse environmental effects of the blast, which can cause serious damage to the nearby buildings and people and even lead to the death in some cases. Therefore, it is very essential to correctly predict the flyrock distance to control and reduce the flyrock and also to determine the safety area. Due to the multiplicity of the parameters influencing the flyrock and the complexity of interacting parameters, the performance of empirical equations for predicting the flyrock distance is not satisfied and they cannot guaranty safety for the surrounding environment. In this research, in the first step, the predictor equation for the blast-induced flyrock was obtained using the gene expression programming. To achieve this goal, the data from 318 blast blocks in the Sungun copper mine was recorded and the amount of flyrock resulting from each blast was accurately measured. Burden, spacing, stemming, charge length and powder factor were used as the input parameters in gene expression programming (GEP) model. Among the gathered datasets, 268 datasets were intended for the model training and 50 datasets for the model testing. Then, the obtained equation from GEP was used as cost function for minimizing flyrock by cuckoo optimization algorithm. For this purpose, five strategies were defined and some optimal blast designs were provided for each strategy. The results showed that the gene expression programming model with the correlation coefficient of 0.91 and the root mean square error of 5.85 m has a good performance in predicting the blast-induced flyrock using the blast design parameters. Also, the maximum flyrock value of blast designs provided by the cuckoo algorithm was reduced by 43.6% relative to the maximum flyrock value of the initial blast designs. Input parameters sensitivity analysis showed that the powder factor is the most effective parameter on the flyrock phenomenon.

关键词： Flyrock Gene expression programming cuckoo optimization algorithm

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Small modular reactor full scope core optimization using cuckoo optimization algorithm

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PROGRESS IN NUCLEAR ENERGY 2020年 122卷 103271-103271页

作者： Akbari, R. Ochbelagh, D. Rezaei Gharib, A. Maiorino, J. R. D'Auria, F. Amirkabir Univ Technol Dept Energy Engn & Phys Tehran Polytech Tehran Iran Univ Pisa GRNSPG DESTEC Pisa Italy Fed Univ ABC CECS Santo Andre SP Brazil

Small Modular Reactors (SMRs) with their excellent safety and economic features will be in high demand in the near future. Most SMR designs have longer burn-up cycle length with more fuel enrichment and smaller core size in comparison to the large conventional nuclear reactors. The small size of these reactors causes more neutron leakage (less core radius results in a higher area to volume ratio and more relative leakage). This feature of SMRs causes high values of maximum Power Peaking Factors (PPFs) through the core, so optimizing the safety parameters is of high necessity. Also, long burn-up cycle length needs a high initial excess reactivity, which results into use of some materials and methods to control this high excess reactivity. One of these methods is using a high number of Integral Fuel Burnable Absorber (IFBA) rods. In the present designs of IFBA rods, usually some amounts of fuel with lower enrichment are used at the top and bottom parts of the IFBA rods (known as cutback fuel) to flatten the axial PPFs. The small size of the SMRs (using a lower number of FAs) helps to have much less possible radial loading patterns (in comparison to the large reactors) and provides the possibility to optimize the axial variations in amounts of cutback fuel in IFBA rods simultaneously. Accordingly, the best axial and radial loading pattern according to the objective functions could be achieved. At the present work, the main goal is to optimize radial core loading pattern and axial variations of cutback fuel lengths at the IFBA rods of an SMR simultaneously using a multi-objective neutronic and thermal-hydraulic fitness function. The multi-objective fitness function includes burn-up cycle length, Minimum Departure from Nucleate Boiling (MDNBR), maximum and average radial and axial PPFs during the entire cycle lengths. The cuckoo optimization algorithm (COA) as a new robust metaheuristic algorithm with high convergence speed and global optima achievement has been used. Fo

关键词： Small modular reactor Neutronic calculation Thermal-hydraulic calculation cuckoo optimization algorithm

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Energy-Aware Clustering-Based Routing in Wireless Sensor Networks Using cuckoo optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第3期98卷 2473-2495页

作者： Khabiri, Melika Ghaffari, Ali Islamic Azad Univ Germi Branch Dept Comp Engn Germi Iran Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Since sensor nodes make use of battery energy, energy consumption and limitation of sensor nodes is regarded as a fundamental challenge and problem in wireless sensor nodes. Recently, in wireless sensor networks (WSNs), clustering-based energy-aware routing protocols divide neighboring nodes into separate clusters and select local cluster heads so as to combine and transmit information of each of the clusters to the central station. In this way, they attempt to maintain energy consumption balance by the network nodes. When compared with other methods, clustering methods have been able to achieve the best efficiency with regard to the enhancement of network lifetime. In this paper, using cuckoo optimization algorithm, an energy-aware clustering-based routing protocol was proposed in WSNs which is able to cluster the network and select optimal cluster heads. The proposed method considered four criteria with regard to selecting cluster heads in the targeted cuckoo algorithm, namely the remaining energy of nodes, distance to the base station, within-cluster distances and between cluster distances. The results of simulating the proposed method in Matlab environment indicated it is better than other algorithms such as low energy adaptive clustering hierarchical (LEACH), application-specific low power routing, LACH-EP and LEACH with distance-based threshold with regard to the first node die on average and packet delivery rate for six scenario.

关键词： Wireless sensor networks (WSNs) Energy-aware routing Clustering cuckoo optimization algorithm Energy consumption

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Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第2期16卷 117页

作者： Hosseininejad, Seyedhadi Dadkhahe, Chitra KN Toosi Univ Technol Fac Comp Engn POB 1355-16315Shariati Ave Tehran *** Iran

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human's interference in a dynamic environment are thriving. A dynamic environment is the robot's environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.

关键词： Autonomous mobile robot path planning cuckoo optimization algorithm dynamic environment

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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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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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Optimal Tuning of PI Controller for Boost DC-DC Converters Based on cuckoo optimization algorithm 7

Optimal Tuning of PI Controller for Boost DC-DC Converters B...

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7th International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Mamizadeh, Ali Genc, Naci Rajabioun, Ramin Van Yuzuncu Yil Univ Elect Elect Engn Dept Van Turkey Van Yuzuncu Yil Univ Elect Elect Engn Dept Van Turkey Electrotech Engn Ltd Co Res & Dev Dept Van Turkey

ISBN: (纸本)9781538659823

Applications of the boost DC-DC converters are becoming popular due to their conformity to renewable energy systems. PI controllers are the most used controllers in power electronic circuit and systems controlling. This paper presents the optimal tuning of Proportional-Integral (P-I) controller for DC-DC boost converters based on cuckoo optimization algorithm (COA). The COA based optimization of boost converter is modeled and simulated in MATLAB/Simulink environment under variable load. Then the simulation results of the COA based tuned PI controller are compared to the classical PI tuning method. The performance of proposed COA based tuning PI was better than conventional PI tuning method.

关键词： boost converter PI tuning optimal tuning cuckoo optimization algorithm

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Optimal Coordination of Directional Overcurrent Relays in Microgrids by Using cuckoo-Linear optimization algorithm and Fault Current Limiter

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IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 1365-1375页

作者： Dehghanpour, Ehsan Karegar, Hossein Kazemi Kheirollahi, Reza Soleymani, Tohid Shahid Beheshti Univ Dept Elect Engn Tehran *** Iran Tarbiat Modares Univ Dept Elect & Comp Engn Tehran 14115116 Iran

Nowadays, distributed generations (DGs) and microgrids play a significant role in development of power systems, but DGs produce several protection problems especially in optimal coordination of directional overcurrent relays (DORs). Optimal coordination of DORs is a highly constrained nonlinear programming problem, which has attracted attention of many research studies in recent years. cuckoo optimization algorithm (COA) is introduced as an optimization tool based on behavior patterns of cuckoos. In this paper, COA and linear programming (LP) are combined as a new hybrid COA-LP optimization algorithm in order to optimize the coordination protection of DORs in microgrids and find the optimum value of fault current limiter at the point of common coupling. The optimum settings of DORs are obtained for both modes of operation: 1) grid-connected and 2) island mode. The proposed algorithm is applied to Canadian distribution benchmark and a modified IEEE 14 bus networks and compared with particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results show that the computational efficiency of the proposed COA-LP is better than PSO and GA in terms of speed and total operating time of DORs.

关键词： Artificial intelligence cuckoo optimization algorithm microgrid protection linear programming protection coordination

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optimization of COCOMO II Coefficients using cuckoo optimization algorithm to Improve The Accuracy of Effort Estimation 11

Optimization of COCOMO II Coefficients using Cuckoo Optimiza...

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11th International Conference on Information and Communication Technology and System (ICTS)

作者： Parwita, I. Made Mika Sarno, Riyanarto Puspaningrum, Alifia Inst Teknol Sepuluh Nopember Dept Informat Engn Surabaya Indonesia

ISBN: (纸本)9781538628270

Software effort estimation becomes an important factor to support the success of software projects development. Uncertainty and complexity of the software can impact to inaccurate estimation directly;it becomes important challenges to improve the accuracy of estimation. Constructive Cost Model (COCOMO) II used two coefficients to estimate the effort of the software. However, using these coefficients for modern projects often leads to inaccurate estimation. To overcome these problems, cuckoo optimization algorithm is proposed to improve the accuracy of the effort estimation by optimizing the coefficients. cuckoo optimization algorithm (COA) is a new metaheuristic method which has been proven for a great optimization. By utilizing the coefficients of effort estimation using COA, the solution then will be evaluated by using Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE). As the result, the research showed the superiority of our proposed method compared to several methods on Turkish Software Dataset.

关键词： COCOMO II Model Coefficients optimization cuckoo optimization algorithm Software Effort Estimation Metaheuristic Method

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