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8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI)

作者： Galvez, Akemi Fister, Iztok Deb, Suash Fister, Iztok, Jr. Iglesias, Andres Univ Cantabria Santander Spain Univ Maribor Maribor Slovenia IT & Educ Consultant Ranchi Bihar India Victoria Univ Melbourne Australia

ISBN: (纸本)9798400717291

Reconstructing the complex behavior of chaotic dynamical systems from time series is a challenging task, due to the high sensitivity to initial conditions and other factors. In this paper, we formulate this issue as an optimization problem, which is addressed through a bio-inspired swarm intelligence technique: the cuckoo search algorithm with Levy flights. The method is applied to reconstruct several periodic and chaotic behaviors of the Henon map, a popular two-dimensional chaotic map. Our graphical and numerical results show that the proposed method is able to obtain suitable values for the parameters under optimization and reconstruct different system behaviors with good accuracy.

关键词： Dynamical systems swarm intelligence cuckoo search algorithm attractor reconstruction chaotic behavior

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cuckoo search algorithm with Balanced Learning to Solve Logistics Distribution Problem 18th

Cuckoo Search Algorithm with Balanced Learning to Solve Logi...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Li, Juan Liu, Han-xia Wuhan Business Univ Sch Informat Engn Wuhan 430058 Peoples R China Wuhan Text Univ Coll Int Business & Econ Wuhan 430202 Peoples R China

ISBN: (纸本)9789819722747;9789819722754

cuckoo search (CS) has been successfully applied to solve various optimization problems. Despite its simplicity and efficiency, the CS is easy to suffer from the premature convergence and fall into local optimum. In this study, a differential CS extension with balanced learning namely DFCS is proposed. In DFCS, two sets including the better fitness set (FSL) and the better diversity set (DSL) are produced in the iterative process. Two excellent individuals are selected from two sets to participate, which improved the search ability by learning their beneficial behaviors in search process. The performance of DFCS algorithm is evaluated on the logistics distribution problem. The results show that DFCS algorithm has stronger competitiveness in solving logistics distribution problem than CS algorithm.

关键词： cuckoo search algorithm Balanced-Learning Optimization algorithm Diversity

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Drag polar modelling for jet aircraft using 6-DOF model data via cuckoo search algorithm

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AERONAUTICAL JOURNAL 2025年第1333期129卷 627-650页

作者： Yurttav, G. Mutlu, T. Baklacioglu, T. Turkish Aerosp Ind TAI TUSAS TR-06980 Ankara Turkiye Eskisehir Tech Univ Fac Aeronaut & Astronaut TR-26555 Eskisehir Turkiye

This paper describes a reverse engineering methodology so as to accomplish an aero-propulsive modelling (APM) through implementing a drag polar estimation for a case study jet aircraft in case of the absence of the thrust data of the aircraft's engine. Since the available thrust force can be replaced by the required thrust force for the sustained turn, this approach allows the elimination for the need of the thrust parameter in deriving an aero-propulsive model utilising equations of motion. Two different modelling approaches have been adopted: (i) implementing the 6-DOF model data for sustained turn and climb flight to achieve induced drag model;and then incorporating the glide data to obtain the total drag polar model;(ii) using the 6-DOF model data together with introducing the effect of C L -alpha dependency. The error assessments showed that the derived CSA models were able to predict the drag polar values accurately, providing linear correlation coefficient (R) values equal to 0.9982 and 0.9998 for the small alpha assumption and C L -alpha dependency, respectively. A direct comparison between the trimmed C D values of 6-DOF model and the values predicted by the CSA model was accomplished, which yielded highly satisfactory results within high subsonic and transonic C L values.

关键词： drag polar modelling aircraft flight phases cuckoo search algorithm optimisation

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A multi-strategy hybrid cuckoo search algorithm with specular reflection based on a population linear decreasing strategy

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第12期15卷 5683-5723页

作者： Ouyang, Chengtian Liu, Xin Zhu, Donglin Zheng, Yangyang Zhou, Changjun Zou, Chengye Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

The cuckoo search algorithm (CS), an algorithm inspired by the nest-parasitic breeding behavior of cuckoos, has proved its own effectiveness as a problem-solving approach in many fields since it was proposed. Nevertheless, the cuckoo search algorithm still suffers from an imbalance between exploration and exploitation as well as a tendency to fall into local optimization. In this paper, we propose a new hybrid cuckoo search algorithm (LHCS) based on linear decreasing of populations, and in order to optimize the local search of the algorithm and make the algorithm converge quickly, we mix the solution updating strategy of the Grey Yours sincerely, wolf optimizer (GWO) and use the linear decreasing rule to adjust the calling ratio of the strategy in order to balance the global exploration and the local exploitation;Second, the addition of a specular reflection learning strategy enhances the algorithm's ability to jump out of local optima;Finally, the convergence ability of the algorithm on different intervals and the adaptive ability of population diversity are improved using a population linear decreasing strategy. The experimental results on 29 benchmark functions from the CEC2017 test set show that the LHCS algorithm has significant superiority and stability over other algorithms when the quality of all solutions is considered together. In order to further verify the performance of the proposed algorithm in this paper, we applied the algorithm to engineering problems, functional tests, and Wilcoxon test results show that the comprehensive performance of the LHCS algorithm outperforms the other 14 state-of-the-art algorithms. In several engineering optimization problems, the practicality and effectiveness of the LHCS algorithm are verified, and the design cost can be greatly reduced by applying it to real engineering problems.

关键词： cuckoo search algorithm Grey wolf optimizer Specular reflection learning Linearly decreasing populations CEC2017 Engineering optimization problems

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Health monitoring of steel structures using cuckoo search algorithm-based ANN

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STRUCTURES 2024年 61卷

作者： Thankachan, Prince Fida, A. Pillai, T. M. Madhavan Natl Inst Technol Calicut Dept Civil Engn Kozhikode 673601 Kerala India

The application of various computational methods in Structural Health Monitoring (SHM) of structures are gaining importance. One of the methodologies used is Artificial Neural Network (ANN). Though ANN can handle complex nonlinear functions that are intractable using traditional methods, the gradient descent nature of back propagation algorithms in ANN traps the solution in local minima, which prohibit it from finding the best solution. This can be resolved using various evolution algorithm combining with Artificial Neural Network, such as cuckoo search (CS) algorithm, Genetic algorithm (GA), Particle Swarm Optimisation (PSO) etc. This study presents a strategy SHM using a flexible combination of ANN and CS algorithm. This nature-inspired metaheuristic algorithm can train the parameters of ANN and can reduce the variation between the actual and predicted output. The strategy is applied on a steel truss bridge and a lattice offshore platform structure and its robustness is compared. Different damage scenarios were also considered. The study demonstrated the superiority of ANN-CS approach over ANN alone. Though ANN predicts the damage with the help of adequate vibrational data, ANN combined with cuckoo search algorithm shows good improvement in predicting the damage with lesser training data set.

关键词： Artificial Neural Network cuckoo search algorithm Health monitoring Steel structures Vibration-based damage detection

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Characterization modeling of wake vortex circulation decay under reasonable worst case conditions with cuckoo search algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2024年第5期238卷 455-472页

作者： Oruc, Ridvan Baklacioglu, Tolga Sahin, Ozlem Agri Ibrahim Cecen Univ Civil Aviat Vocat Sch Agri Turkiye Eskisehir Tech Univ Fac Aeronaut & Astronaut 2 Eylul Kampusu Havacil & Uzay Bil Fak TR-26555 Eskisehir Turkiye Turkish Aerosp Ind TUSAS Aircraft Performance Dept TAI Ankara Turkiye

Wake vortex (WV) produced by a large aircraft has the potential to cause serious damage to smaller aircraft following it. In this context, characterization of WV circulation decay under the reasonable worst case (RWC) conditions allows the separation minima to be found safely. In this study, modeling of dimensionless decay curves, which were developed using three experimental LIDAR (Light Detection and Ranging) datasets in the RECAT-EU project and is a useful tool to characterize the wake vortex circulation decay under RWC conditions, was carried out using cuckoo search algorithm (CSA). The decay curves used in the modeling are the median (P50), 10th (P10), and 90th (P90) percentile decay curves of the RWC tracks, which constitute the top 2% longest lasting wakes. The fact that the correlation coefficient (R) values are very close to 1 for all datasets as a result of the error analysis shows that the prediction success of the CSA model is quite high.

关键词： Aircraft wake vortex turbulence cuckoo search algorithm optimization

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An improved cuckoo search algorithm for global optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第6期27卷 8595-8619页

作者： Tian, Yunsheng Zhang, Dan Zhang, Hongbo Zhu, Juan Yue, Xiaofeng Changchun Univ Technol Sch Mech & Elect Engn Changchun Jilin Peoples R China State Grid Jilin Elect Power Res Inst Changchun Peoples R China

cuckoo search (CS) algorithm is a classical swarm intelligence algorithm widely used in a variety of engineering optimization problems. However, its search accuracy and convergence speed still have a lot of room for improvement. In this paper, an improved version of the CS algorithm based on intelligent perception strategy, adaptive invasive weed optimization (AIWO), and elite cross strategy, called IIC-CS is proposed. Firstly, the intelligent perception strategy can update the value according to the searching state. Moreover, the CS is hybridized with the AIWO to improve the searching performance of the algorithm. Additionally, the elite cross strategy is employed to enhance the exploration capability and exploitation capability of the algorithm. Combining the improvements of these three methods, the performance of the CS algorithm is significantly improved. Meanwhile, 23 classical benchmark functions, some CEC2014 and CEC2018 benchmark functions are used to test the search accuracy and convergence rate of the IIC-CS. Furthermore, some classical or state-of-the-art algorithms such as the genetic algorithm (GA), particle swarm optimization (PSO), bat algorithm (BA), ant lion optimizer (ALO) and cuckoo search (CS) algorithm, invasive weed optimization (IWO), integrated cuckoo search optimizer (ICSO) and improved island cuckoo search (iCSPM2) are used to make comparisons. Through the statistical results of the experiments, we find that the IIC-CS algorithm can achieve better results on most benchmark functions compared to other algorithms, thus demonstrating the effectiveness of the improvements and the superiority of the IIC-CS algorithm.

关键词： cuckoo search algorithm Global optimization Intelligent perception strategy Adaptive invasive weed optimization Elite cross strategy

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Availability evaluation of solar photovoltaic systems using markov modeling and cuckoo search algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 2261-2272页

作者： Kumar, Ashish Maan, Vijay Singh Choudhary, Ravi Saini, Monika Manipal Univ Jaipur Dept Math & Stat Jaipur 303007 Rajasthan India

The main objective of present investigation is to evaluate and optimize the operational availability of the solar photovoltaic systems. As the solar energy is a prominent source of renewal energy and contribute a lot in global development having less environmental impacts but the safety and reliability issues of these systems also observed during the operational phase. Availability is an effective tool that is used to discourse the safety and performance issues of renewal energy sources especially solar photovoltaic systems. Here, a stochastic model is developed for solar photovoltaic system having solar photovoltaic plates, solar charger, solar battery, and inverter. The Markov birth-death process is applied for development of the mathematical model of the proposed system. The chapman-Kolmogorov differential difference equations of the proposed solar photovoltaic system used to predict the steady state availability of system. On the basis of literature, the failure and repair rates of all components of solar photovoltaic system are considered as exponentially distributed. In addition, an effort is also made to predict the optimum availability of solar photovoltaic system using well-known optimization technique cuckoo search algorithm. It is revealed that, the predicted availability of the solar photovoltaic system is 0.9988799 at population size 60 after 700 iterations. The estimated parametric values of the failure and repair rates also derived. To highlight the importance of the study the numerical and graphical results are presented and shared with the system designers and maintenance engineers.

关键词： Renewal energy sources solar photovoltaic systems markov models cuckoo search algorithm availability

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MOBCSA: Multi-Objective Binary cuckoo search algorithm for Features Selection in Bioinformatics

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IEEE ACCESS 2024年 12卷 21840-21867页

作者： Abdulwahab, Hudhaifa Mohammed Ajitha, S. Saif, Mufeed Ahmed Naji Murshed, Belal Abdullah Hezam Ghanem, Fahd A. Univ Sci & Technol Dept Comp Sci & IT Taizz Yemen VTU Ramaiah Inst Technol Dept Comp Applicat Bengaluru 560054 Karnataka India VTU Sri Jayachamarajendra Coll Engn Dept Comp Applicat JSS TI Campus Mysore 570006 Karnataka India Amran Univ Coll Engn Dept Comp Sci & IT Amran Yemen Mysore Univ Dept Studies Comp Sci Mysore 570006 Karnataka India Mysore Univ PES Coll Engn Dept Comp Sci & Engn Mandya 571401 India Hodeidah Univ Coll Educ Zabid Dept Comp Sci Al Hudaydah Yemen

In bioinformatics, medical diagnosis models might be significantly impacted by high-dimensional data generated by high-throughput technologies. This data includes redundant or irrelevant genes, making it challenging to identify the relevant genes from such high-dimensional data. Therefore, an effective feature selection (FS) technique is crucial to mitigate dimensionality, thereby enhancing the performance and accuracy of medical diagnosis. The cuckoo search algorithm (CSA) has proven effective in gene selection, demonstrating prowess in exploitation, exploration, and convergence. However, most of the current CSA-based FS techniques deal with gene selection problems as a single objective rather than adopting a multi-objective mechanism. This article proposes the Multi-Objective Binary cuckoo search algorithm (MOBCSA) for gene selection. MOBCSA extends the standard CSA by incorporating multiple objectives, including accuracy of classification and number of selected genes. MOBCSA utilizes an S-shaped transfer function for transforming the algorithm's search space from a continuous to a binary search space. MOBCSA integrates two components: an external archive to save the pareto optimal solutions attained during the search process, and an adaptive crowding distance updating mechanism integrated into the archive to maintain diversity and increase the coverage of optimal solutions. To assess MOBCSA's performance, evaluation experiments were conducted on six benchmark biomedical datasets using three different classifiers. Then, the obtained experimental results were compared against four multi-objective-based state-of-the art FS methods. The findings prove that MOBCSA surpasses the other methods in both accuracy of classification and number of selected genes, where it has obtained an average accuracy ranging from 92.79% to 98.42% and an average number of selected genes ranging from 15.67 to 27.88 for different classifiers and datasets.

关键词： Features selection multi-objective optimization cuckoo search algorithm machine learning data mining bioinformatics

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Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm

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Defence Technology（防务技术） 2023年第6期24卷 285-297页

作者： De-jiang Lu Xing Wang Xiao-tian Wu You Chen Aviation Engineering School Air Force Engineering UniversityXi'an710038China

The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is *** adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm(DADCS)is proposed,whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar ***,considering the information fusion rules and different working modes of the netted radar system,a two-factor jamming effectiveness evaluation function is constructed,detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode,*** a nonconvex optimization model for cooperatively jamming netted radar system is ***,a dynamic adaptive discrete cuckoo search algorithm(DADCS)is constructed by improving path update strategies and introducing a global learning mechanism,and a three-step solution method is proposed *** results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.

关键词： cuckoo search algorithm Netted radar system Radar countermeasures Resource allocation Information fusion

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