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A Well-Structured Metaheuristic optimization Technique for Magnetic Data Inversion of 2D Dipping Dyke-like Geological Structures Using the cuckoo optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第9期50卷 6663-6672页

作者： Toushmalani, Reza Essa, Khalid S. Ibraheem, Ismael M. Islamic Azad Univ Coll Skills & Entrepreneurship Rasht Branch Rasht Iran Cairo Univ Fac Sci Geophys Dept PO 12613 Giza Egypt Univ Cologne Inst Geophys & Meteorol Pohligstr 3 D-50969 Cologne Germany

This study applies the cuckoo optimization algorithm (COA), inspired by the brood reproduction technique of cuckoo birds, to interpret magnetic anomalies of 2D dipping dyke-like structures. The primary issue addressed is the need for accurate delineation and explanation of dyke parameters, which are crucial for visualizing dyke propagation (important for volcanic hazard assessment), tracing mineralized zones associated with dykes, and understanding their geodynamic significance. Our method identifies dyke parameters at the minimum value of the suggested objective function, ensuring the best fit. The proposed COA method was tested on both noise-free numerical magnetic datasets and datasets with varying levels of random noise (5%, 10%, and 20%), as well as real-case datasets from China and the UK. A comparative analysis with particle swarm optimization (PSO) and genetic algorithm (GA) methods was conducted to evaluate the efficiency and consistency of COA. The results demonstrate that COA aligns well with existing geological and geophysical information, offering superior accuracy and robustness compared to traditional techniques. This study provides a novel and effective approach for subsurface characterization, advancing the precision of geological and geophysical interpretations.

关键词： cuckoo optimization algorithm Dipping dyke Depth estimation Mineral exploration

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cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

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WASTE MANAGEMENT & RESEARCH 2022年第4期40卷 458-469页

作者： Shadkam, Elham Khayyam Univ Fac Engn Dept Ind Engn POB *** Mashhad Razavi Khorasan Iran

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts - factory, consumers' and recycling centres - each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.

关键词： Network design direct and reverse logistics waste management coronavirus (COVID-1) vaccine cuckoo optimization algorithm

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cuckoo optimization algorithm for unit production cost in multi-pass turning operations

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期76卷 647-656页

作者： Mellal, Mohamed Arezki Williams, Edward J. MHamed Bougara Univ Fac Engn Sci FSI Boumerdes 35000 Algeria Univ Michigan Coll Engn & Comp Sci Ind & Mfg Syst Engn Dept Dearborn MI 48126 USA Univ Michigan Coll Business Dearborn MI 48126 USA

The multi-pass turning process is one of the most used machining methods in manufacturing industry. The minimization of the unit production cost is considered the key objective of this operation. In this work, the cutting parameters are carried out using a recently developed advanced bio-inspired optimization algorithm, called the cuckoo optimization algorithm (COA). The obtained results are compared with previously published results available in the literature. It has been proven that the COA competes robustly with a wide range of optimization algorithms.

关键词： Multi-pass turning operations Cutting parameters Unit production cost cuckoo optimization algorithm

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Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator

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SOFT COMPUTING 2021年第4期25卷 2911-2933页

作者： Kelidari, Mahsa Hamidzadeh, Javad Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

Feature selection, which plays an important role in high-dimensional data analysis, is drawing increasing attention recently. Finding the most relevant and important features for classifications are one of the most important tasks of data mining and machine learning, since all of the datasets have irrelevant features that affect accuracy rate and slow down the classifier. Feature selection is an optimization process, which improves the accuracy rate of data classification and reduces the number of selected features. Applying too many features both requires a large memory capacity and leads to a slow execution speed. Feature selection algorithms are often responsible to decide which features should be selected to be used during a classification algorithm. Traditional algorithms seemed to be inefficient due to the complexity of dimensions of the problem, thus evolutionary algorithms were used to improve the problem solving process. The algorithm proposed in this paper, chaotic cuckoo optimization algorithm with levy flight, disruption operator and opposition-based learning (CCOALFDO), is applied to select the optimal feature subspace for classification. It reduces the randomization in selecting features and avoids getting stuck in local optimum solutions which lead to a more interesting feature subset. Extensive experiments are conducted on 20 high-dimensional datasets to demonstrate the effectiveness and efficiency of the proposed method. The results showed the superiority of the proposed method to state-of-the-art methods in terms of classification accuracy rate. In addition, they prove the ability of the CCOALFDO in selecting the most relevant features for classification tasks. Thus, it is a reasonable solution in handling noise and avoiding serious negative impacts on the classification accuracy rate in real world datasets.

关键词： Feature selection High-dimensional data cuckoo optimization algorithm Chaotic theory Levy flight Disruption operator Opposition-based learning

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Improved cuckoo optimization algorithm for solving systems of nonlinear equations

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JOURNAL OF SUPERCOMPUTING 2016年第3期72卷 1246-1269页

作者： Abdollahi, Mahdi Bouyer, Asgarali Abdollahi, Davoud Islamic Azad Univ Miandoab Branch Dept Comp Engn Miandoab Iran Azarbaijan Shahid Madani Univ Fac Comp Engn & Informat Technol Tabriz Iran Univ Coll Daneshvaran Tabriz Iran

Systems of nonlinear equations come into different range of sciences such as chemistry, economics, medicine, robotics, engineering, and mechanics. There are different methods for solving systems of nonlinear equations such as Newton type methods, imperialist competitive algorithm, particle swarm algorithm, conjugate direction method that each has their own advantages and weaknesses such as low convergence speed and poor quality of solutions. This paper improves cuckoo optimization algorithm for solving systems of nonlinear equations by changing the policy of egg laying radius, and some well-known problems are presented to demonstrate the efficiency and better performance of this new robust optimization algorithm. From obtained results, our approach found more accurate solutions with the lowest number of function evaluations.

关键词： cuckoo optimization algorithm Evolutionary algorithms Nonlinear equations optimization

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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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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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Optimum Operation of Reservoir Using Two Evolutionary algorithms: Imperialist Competitive algorithm (ICA) and cuckoo optimization algorithm (COA)

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WATER RESOURCES MANAGEMENT 2015年第10期29卷 3749-3769页

作者： Hosseini-Moghari, Seyed-Mohammad Morovati, Reza Moghadas, Mohammad Araghinejad, Shahab Univ Tehran Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Coll Agr & Nat Resources Karaj Iran Imam Khomeini Int Univ Water Engn Dept Qazvin Iran

Water scarcity is one of the most serious problems in many parts of the world that affects negatively on the environment, society, and economy. In order to mitigate the negative effects of this issue, optimal water resource management is pivotal. In current paper, imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA) which they are two new evolutionary methods, were used in optimal operation of reservoir. Firstly, these algorithms were used in solving several benchmark problems. Afterwards, optimal operation policy of Karun4 reservoir was extracted. Karun4 is located in Chaharmahal Va Bakhtiari province in western of Iran. Finally, the results which obtained from these methods were compared with genetic algorithm (GA) and nonlinear programing (NLP). In benchmark problems, COA converges to optimal point appropriately well and shows best performance. In these problems, ICA represent suitable ability to achieve global optimum. Both COA and ICA algorithms showed high performance in extraction of optimal operation policies from Karun4, which was conducted over a period of 360 months, with the aim of maximizing productivity. COA indicated the best performance with average value of 5.454 for objective function, and ICA with 6.461 value was at the second rank. In addition, GA objective function value was 6.869. Also NLP solver of Lingo11 was used in order to optimal operation of Karun4 reservoir for evaluating the ability of these algorithms to achieve global solution. Objective function value which was gained by NLP method was 5.243. The results reflect the strength of COA in approaching global optimum.

关键词： cuckoo optimization algorithm Genetic algorithm Imperialist competitive algorithm Karun4 reservoir Optimal operation

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Assessing the suitability of hybridizing the cuckoo optimization algorithm with ANN and ANFIS techniques to predict daily evaporation (Publication with Expression of Concern)

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ENVIRONMENTAL EARTH SCIENCES 2016年第3期75卷 1-13页

作者： Piri, Jamshid Mohammadi, Kasra Shamshirband, Shahaboddin Akib, Shatirah Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Massachusetts Dept Mech & Ind Engn Amherst MA 01003 USA Univ Zabol Soil & Water Coll Dept Water Engn Zabol Iran Heriot Watt Univ Sch Energy Geosci Infrastruct & Soc EGIS Wilayah Persekutuan Putrajaya Malaysia

Estimation of evaporation is of indispensable significance for management and development of water resources. This study aims to identify the suitability of hybridizing the cuckoo optimization algorithm (COA) with two well-known approaches of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for prediction of daily pan evaporation. For this aim, two hybrid models of ANN-COA and ANFIS-COA are developed and their performances are compared with single ANN and ANFIS. As case study, the daily climate parameters including the average air temperature (T-avg), sunshine hours (S), relative humidity (R-h), wind speed (W) and pan evaporation (E) measured and collected for three Iranian stations of Zabol, Iranshahr and Shiraz have been utilized. The used data sets are divided into three parts so that 60, 20 and 20 % of the data are applied for training, testing and prediction phases, respectively. The achieved results prove that the models' performances are variable among cities. It is found that combining the COA with ANN and ANFIS techniques does not enhance the precision of the developed ANN and ANFIS models noticeably in all considered stations. In fact, the results demonstrate that hybridizing the COA with ANN and ANFIS cannot be a viable option for estimation of daily evaporation. Overall, the study results indicate that further accuracy can generally be achieved by the ANN model;consequently, the ANN model can be sufficiently used in the prediction of daily evaporation.

关键词： Daily evaporation cuckoo optimization algorithm ANN ANFIS Prediction

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Total production time minimization of a multi-pass milling process via cuckoo optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期87卷 747-754页

作者： Mellal, Mohamed Arezki Williams, Edward J. MHamed Bougara Univ Fac Engn Sci FSI LMSS Boumerdes 35000 Algeria Univ Michigan Coll Business Decis Sci Dearborn MI 48126 USA Univ Michigan Ind & Mfg Syst Engn Dept Coll Engn & Comp Sci Dearborn MI 48126 USA

The milling process is a widely used conventional machining operation. Due to economic reasons, the multi-pass milling process is more convenient. However, the required time for machining increases and an optimization solution must be undertaken. In this paper, the total production time is minimized by resorting to a powerful bio-inspired algorithm, called the cuckoo optimization algorithm. The constraints are successfully handled and the optimal results are compared with those available in the literature. It is shown that the present results are better.

关键词： Multi-pass milling process Total production time Machining parameters cuckoo optimization algorithm

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