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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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A new accurate and fast convergence cuckoo search algorithm for solving constrained engineering optimization problems

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 2307-2337页

作者： Abdollahi, Mahdi Bouyer, Asgarali Arasteh, Bahman Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan

In recent years, the cuckoo optimization algorithm (COA) has been widely used to solve various optimization problems due to its simplicity, efficacy, and capability to avoid getting trapped in local optima. However, COA has some limitations such as low convergence when it comes to solving constrained optimization problems with many constraints. This study proposes anew modified and adapted version of the cuckoo optimization algorithm, referred to as MCOA, that overcomes the challenge of solving constrained optimization problems. The proposed adapted version introduces a new coefficient that reduces the egg-laying radius, thereby enabling faster convergence to the optimal solution. Unlike previous methods, the new coefficient does not require any adjustment during the iterative process, as the radius automatically decreases along the iterations. To handle constraints, we employ the Penalty Method, which allows us to incorporate constraints into the optimization problem without altering its formulation. To evaluate the performance of the proposed MCOA, we conduct experiments on five well-known case studies. Experimental results demonstrate that MCOA outperforms COA and other state-of-the-art optimization algorithms in terms of both efficiency and robustness. Furthermore, MCOA can reliably find the global optimal solution for all the tested problems with in a reasonable iteration number.

关键词： Nonlinear optimization problem constrained problems engineering designing problems penalty function cuckoo optimization algorithm

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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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Optimal synchronization of teleoperation systems via cuckoo optimization algorithm

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NONLINEAR DYNAMICS 2014年第4期78卷 2359-2376页

作者： Shokri-Ghaleh, H. Alfi, A. Shahrood Univ Technol Fac Elect & Robot Engn Shahrood *** Iran

The main goal of controller design in bilateral teleoperation systems is to achieve stability and transparency in presence of different factors such as time delay in communication channel and modeling uncertainties. The contribution of this paper is designing an optimal controller for synchronization of bilateral teleoperation systems with the objectives of reducing the factors. This requires optimizing a set of parameters of the so-called synchronization control law. To this reason, a novel meta-heuristic algorithm namely cuckoo optimization (CO) algorithm was employed. Comparative simulations are performed to demonstrate the feasibility of the proposed control technique. The results indicate that CO is a powerful search and optimization technique that may yield better solutions to the problem in hand than those obtained using other algorithms including biogeography-based optimization, imperialist competitive and artificial bee colony.

关键词： Bilateral control Transparency Stability optimization cuckoo optimization algorithm

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A New Routing Protocol for Energy optimization in Mobile Ad Hoc Networks Using the cuckoo optimization and the TOPSIS Multi-Criteria algorithm

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CYBERNETICS AND SYSTEMS 2021年第6期52卷 477-497页

作者： Tabatabaei, Shayesteh Higher Educ Complex Saravan Dept Comp Engn Saravan Iran

The Mobile Ad Hoc Network (MANET) is a set of ad hoc nodes without any infrastructure that form a temporary network without the presence of central control. Nodes present in such networks are usually in movement and result in frequent topological changes. The design of proper routing protocols to control dynamic topological changes in an ad hoc network can enhance the performance of a network. In this regard, the current study attempts to present a new routing protocol to improve energy consumption using the cuckoo optimization algorithm and the TOPSIS multi-criteria algorithm. The proposed approach can be applied in dynamic environments, and four important variables including the accessible bandwidth, the remaining energy, the speed of movement, and the number of steps necessary for routing have been considered for it. To select proper groups for the purpose of relaying data packages, the TOPSIS multi-criteria algorithm was applied. In addition, the cuckoo optimization algorithm was used to select the shortest route between the relay groups. The simulation results showed that the process of selecting a stable route using the cuckoo optimization algorithm and the TOPSIS algorithm has a significant impact on the performance of networks, and the proposed algorithm shows better performance compared to the methods introduced in Tabatabaei and Behravesh with regard to the throughput and the end-to-end delay.

关键词： cuckoo optimization algorithm energy consumption mobile ad hoc network routing algorithm TOPSIS multi-criteria algorithm

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A hybrid approach based on artificial neural network and cuckoo algorithm for optimization of the main cutting force during turning of Si brass alloys

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SN APPLIED SCIENCES 2021年第1期3卷 100页

作者： Adineh, Morteza Doostmohammadi, Hamid Shahid Bahonar Univ Kerman Sch Engn Dept Met & Mat Sci Jomhoori Eslami Blvd Kerman Iran

As an innovative approach in this study, artificial neural network and cuckoo algorithm have been applied to estimate and optimize the main cutting forces of various Si brass alloys during turning operation due to economic reasons. Accordingly, the chemical composition (Cu, Zn and Si contents) and process parameters (cutting speed, feed rate and depth of cut) are simultaneously implemented as input variables and the main cutting force is adjusted as an output variable. Moreover, the genetic algorithm is used to determine the optimum condition of the input parameters to obtain the lowest amounts of the main cutting force. Coupling of the hybrid cuckoo algorithm with artificial neural network has resulted in decreasing the mean absolute percentage error of the optimum structure (6-10-7-1) from 9.025 to 1.59E-6%. The validation of the proposed model has been done by performing the new set of experimental tests. The measured and predicted main cutting forces are in good agreement. The Si brass alloys including Zn equivalent about 44.97 wt% has the lowest main cutting force due to the formation of the Widmanstaetten morphologies in the microstructure. The outcome of this study may be useful for machining industry of the free-cutting Si brasses.

关键词： Silicon brass Artificial neural network cuckoo optimization algorithm Main cutting force

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A new hybrid fuzzy bio-inspired classifier for cancer detection using cuckoo optimization and hyper-planes

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DATA TECHNOLOGIES AND APPLICATIONS 2025年第3期59卷 416-451页

作者： Abdolrazzagh-Nezhad, Majid Izadpanah, Shaghayegh Birjand Univ Technol Fac Comp & Ind Engn Dept Comp Sci Birjand Iran Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran

PurposeVarious methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for comparing a new patient's information with the aggregated data to detect cancer. The main step in this process is data classification. There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical ***/methodology/approachIn this paper, a new fuzzy bio-inspired-based classification method is designed to classify the imbalance of medical data. The method consists of a new fuzzy draft of the cuckoo optimization algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that are called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy cuckoo optimization algorithm (FCOA), which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity *** experimental results were presented in five steps. Step1, the details of the average and the best results of the proposed methods were reported and compared. Step2, the quality of the detection methods with different numbers of hyper-planes were compared. The obtained weights of different numbers of hyper-planes were reported in Step3. Step4, the convergence process of the FCOA and the COA were shown. Step5, the best obtained results were compared with the best reported one in previous literature. The experimental results and the presented comparisons show that the proposed hybrid detection method is comparable to other methods and operates better than them in most ***/valueA technical r

关键词： Cancer detection Hyper-planes classifier cuckoo optimization algorithm Fuzzy inference system Data classification

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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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AN ADAPTED cuckoo optimization algorithm AND GENETIC algorithm APPROACH TO THE UNIVERSITY COURSE TIMETABLING PROBLEM

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2014年第1期13卷 1450002-1450002页

作者： Teoh, Chong Wibowo, Antoni Ngadiman, Mohd. Univ Teknol Malaysia Dept Comp Sci Fac Comp UTM Johor Baharu 81310 Johor Malaysia

The university course timetabling problem is an NP-hard and NP-complete problem concerned with assigning a specific set of events and resources to timeslots under a highly-constrained search space. This paper presents a novel metaheuristic algorithm entitled adapted cuckoo optimization algorithm which is derived from the cuckoo optimization algorithm and cuckoo search algorithm. This algorithm includes features such as local random walk on discrete data which mimics the behavior of Levy flights and an Elitism-based mechanism which echoes back the best candidate solutions and prevents the algorithm from plunging into a curse of dimensionality. The algorithm was tested on a problem instance gathered from a University in Malaysia and the results indicate that the proposed algorithm exhibits very promising results in terms of solution quality and computational speed when compared to genetic algorithms.

关键词： University course timetabling problem genetic algorithms cuckoo optimization algorithm cuckoo search

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Distribution Feeder Reconfiguration Considering Distributed Generators using cuckoo optimization algorithm

Distribution Feeder Reconfiguration Considering Distributed ...

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The 2nd International Conference on Research in Science, Engineering and Technology (ICRSET’2014, Dubai)

作者： Mohamad Amiri Mina Sheikholeslami Kandelousi Mahmood Moghadasian Islamic Azad University Abadan Branch

In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units(DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration(DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the cuckoo optimization algorithm(COA) to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm(GA), and Tabu search(TS) over a realistic distribution test system.

关键词： cuckoo optimization algorithm Distributed generator Distribution feeder reconfiguration Distribution networks

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