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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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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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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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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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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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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

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 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 multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 60卷 234-248页

作者： Akbari, Mehdi Rashidi, Hassan Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Allameh Tabatabai Univ Dept Math & Comp Sci Tehran Iran

To handle scheduling of tasks on heterogeneous systems, an algorithm is proposed to reduce execution time while allowing for maximum parallelization. The algorithm is based on multi-objective scheduling cuckoo optimization algorithm (MOSCOA). In this algorithm, each cuckoo represents a scheduling solution in which the ordering of tasks and processors allocated to them are considered. In addition, the operators of cuckoo optimization algorithm means laying and immigration are defined so that it is usable for scheduling scenario of the directed acyclic graph of the problem. This algorithm adapts cuckoo optimization algorithm operators to create proper scheduling in each stage. This ensures avoiding local optima while allowing for global search within the problem space for accelerating the finding of a global optimum and delivering a relatively optimized scheduling with the least number of repetitions. Moving toward global optima is done through a target immigration operator in this algorithm and schedules in each repetition are pushed toward optimized schedules to secure global optima. The results of MOSCOA implementation on a large number of random graphs and real-world application graphs with a wide range characteristics show MOSCOA superiority over the previous task scheduling algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Task scheduling Heterogeneous systems cuckoo optimization algorithm Meta-heuristic algorithm Makespan

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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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