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6th International Conference on Modeling, Simulation, and Applied optimization (ICMSAO)

作者： Yakhchi, Moona Ghafari, Seyed Mohssen Yakhchi, Shahpar Fazeli, Mahdi Patooghi, Ahmad Islamic Azad Univ Borujerd Branch Dept Comp Engn Borujerd Iran Univ Sci & Technol Compuer Engn Sch Iran Tehran Iran

ISBN: (纸本)9781467366014

With rapid increasing demand of cloud computing technology, energy efficiency has become highly important in cloud computing infrastructures. Cloud computing concept offers low cost and high level of availability. However, it still has some challenging problems, such as resource management and power consumption. In this concept, reducing energy consumption and maximize resource utilization, became a primary concerns of many resource management methods. In this paper, we presented an approach based on cuckoo optimization algorithm (COA) to detect over-utilized hosts. Following that, we employed The Minimum Migration Time (MMT) policy to migrate Virtual Machines (VMs) from the over-utilized hosts to the other hosts. Meanwhile, the migration process should not make any more over-utilized host. Finally, we considered all the hosts, except the over-utilized ones, as the underutilized hosts. At that point, we tried to migrate all the VMs which been allocated to the underutilized hosts to the other hosts and switch them to the sleep mode. The Simulation results which generated by Cloudsim simulator, demonstrate that the proposed approach has lowest energy consumption compared to the other famous algorithms like MAD-MMT(Median Absolute Deviation-Minimum Migration Time), IQR-MMT(Interquartile Range-Minimum Migration Time), Bee-MMT(Bee colony algorithm-Minimum Migration Time), LR-MMT(local Regression-Minimum Migration Time) and non-power aware.

关键词： cloud computing cuckoo optimization algorithm resource allocation

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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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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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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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Improving cuckoo optimization algorithm for solving multimodal problems and non-cooperative Games 9

Improving cuckoo optimization algorithm for solving multimod...

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9th International Conference on Information and Knowledge Technology (IKT)

作者： Lomer, Fatemeh Abedi Saffari, Rahebeh Mojtahedi Islamic Azad Univ Coll Technol & Engn Dept Comp Engn Lahijan Branch Lahijan Iran

ISBN: (纸本)9781538625477

There are several optimal solutions for a multimodal optimization problem. In fact, a multimodal problem has several optimal points and all these points help to find the solution. In this paper, a new algorithm based on cuckoo optimization algorithm is presented for solving the multimodal problems. In the proposed algorithm, the multimodal optima are searched in a separate group of cuckoos which have no coevolution relation. The performance of the proposed algorithm on the multimodal testing functions has shown the proper results in finding the optimal points and also reducing the number of function assessments. As a practical matter and closeness to the real world problems, finding the Nash equilibrium points in the noncooperative several-individual games is categorized under hard problems which the mathematical techniques cannot identify all the equilibrium points to solve. The results of proposed method indicate its performance compare to other methods in finding all parts of the Nash equilibrium points.

关键词： evolutionary algorithms multimodal optimization cuckoo optimization algorithm Nash Equilibrium

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Entropy based optimal multilevel thresholding using cuckoo optimization algorithm 11

Entropy based optimal multilevel thresholding using cuckoo o...

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11th International Conference on Innovations in Information Technology (IIT)

作者： Mousavirad, Seyed Jalaleddin Ebrahimpour-Komleh, Hossein Univ Kashan Fac Comp & Elect Engn Dept Comp Engn Kashan Iran

ISBN: (纸本)9781467385114

Image thresholding considered as a popular method for image segmentation. So far, many approaches have been proposed for image thresholding. Maximum entropy thresholding has been widely applied in the literature. This paper proposes a multilevel image thresholding (MECOAT) using cuckoo optimization algorithm (COA). COA is a new nature-based optimization algorithm which is inspired by a bird named cuckoo. This algorithm is based unusual egg laying and breeding of cuckoos. MECOAT tries to maximize entropy criterion. Three different algorithms are compared with MECOAT algorithm: particle swarm optimization, genetic algorithm, and bat algorithm. Experimental results indicate that MECOAT presents better results in terms of fitness value, peak signal to noise ratio (PSNR) and robustness in most cases.

关键词： cuckoo optimization algorithm Multilevel thresholding Image segmnetation maximum entropy

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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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Solving a multi-objective resource-constrained project scheduling problem using a cuckoo optimization algorithm

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SCIENTIA IRANICA 2014年第6期21卷 2419-2428页

作者： Ghamginzadeh, A. Najafi, A. A. Azimi, P. Islamic Azad Univ Fac Ind & Mech Engn Qazvin Branch Qazvin Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

Scheduling is an important factor in project success. In the real world atmosphere, project scheduling problems involve multiple objectives which must be optimized simultaneously. According to the literature, several meta-heuristic algorithms have been used in single objective resource-constrained project scheduling problems, but very few of them have used a multi-objective framework. In this study, we focus on a multi-objective resource-constrained project scheduling problem by minimizing project completion time and the time value of project costs, which is the main contribution of the current research. The goal is to provide an algorithm that can find optimum Pareto front solutions, using a multi-objective cuckoo optimization algorithm. In order to increase the efficiency of the algorithm, the algorithm parameters are tuned using Taguchi tests. Finally, the solutions derived from the algorithm have been compared to those obtained from NSGA-II. The experiments show the efficiency of the proposed algorithm. (C) 2014 Sharif University of Technology. All rights reserved.

关键词： Project scheduling Net present value cuckoo optimization algorithm

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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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A flexibility-based method via the iterated improved reduction system and the cuckoo optimization algorithm for damage quantification with limited sensors

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SMART MATERIALS AND STRUCTURES 2014年第4期23卷

作者： Hosseinzadeh, Ali Zare Bagheri, Abdollah Amiri, Gholamreza Ghodrati Koo, Ki-Young Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Engn Tehran Iran Univ Pittsburgh Dept Civil & Environm Engn Pittsburgh PA 15261 USA Univ Exeter Vibrat Engn Sect Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

In this paper, a novel and effective damage diagnosis algorithm is proposed to localize and quantify structural damage using incomplete modal data, considering the existence of some limitations in the number of attached sensors on structures. The damage detection problem is formulated as an optimization problem by computing static displacements in the reduced model of a structure subjected to a unique static load. The static responses are computed through the flexibility matrix of the damaged structure obtained based on the incomplete modal data of the structure. In the algorithm, an iterated improved reduction system method is applied to prepare an accurate reduced model of a structure. The optimization problem is solved via a new evolutionary optimization algorithm called the cuckoo optimization algorithm. The efficiency and robustness of the presented method are demonstrated through three numerical examples. Moreover, the efficiency of the method is verified by an experimental study of a five-story shear building structure on a shaking table considering only two sensors. The obtained damage identification results for the numerical and experimental studies show the suitable and stable performance of the proposed damage identification method for structures with limited sensors.

关键词： damage prognosis incomplete modal data flexibility matrix static displacement iterated improved reduction system cuckoo optimization algorithm

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