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Colony-Based Search algorithm for numerical optimization

APPLIED SOFT COMPUTING 2024年 151卷

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkiye Erciyes Univ Fac Engn Dept Biomed Engn Kayseri Turkiye

The efficiency of an evolutionary algorithm is highly sensitive to the mutation and crossover operators it possesses, as well as to the strategy used for determining the direction of numerical evolution and the values of evolutionary step sizes. There is no analytical method to efficiently define the direction of numerical evolution and the value of evolutionary step size for EAs. The efficiency of EAs' search processes is also influenced by their ability to maintain numerical diversity within the population. This paper introduces the Colony-Based Search algorithm (CSA). The development of CSA was motivated by the scientific and industrial need for a relatively more efficient EA. CSA possesses relatively more efficient artificial genetic operators and strategies for producing evolutionary direction and step size, and the ability to maintain numerical diversity. CSA generates the Clan Matrix containing the pattern vectors to be evolved in the current iteration by randomly selecting pattern vectors from the Colony Matrix at the beginning of each iteration. This makes it easier for CSA to maintain numerical diversity among pattern vectors for a long time. CSA's mutation method includes three randomly blended components with different properties. The problem-solving performance of CSA is statistically compared with the problem-solving performance of eight popular evolutionary search methods (i.e., SADE, SHADE, LSHADE, COBIDE, JADE, CK, GWO, and SFS) by using benchmark functions of CEC'2017 and CEC'2022. In the experiments, the 3D viewshed analysis was addressed as a real-world problem, employing the CSA. The statistical analyses conducted on the experimental results indicate that CSA performs relatively better than the compared methods to solve numerical problems.

关键词： differential evolution algorithm Particle Swarm algorithm Gray Wolf Optimizer Viewshed analysis

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Order Sequencing Problem in a Robotic Mobile Fulfillment System 34

Order Sequencing Problem in a Robotic Mobile Fulfillment Sys...

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Hanqi Zhang, Yaoxin Xiao, Zhiguo Li, Dongni Beijing Inst Technol Sch Comp Sci Beijing 100081 Peoples R China Changchun Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Beijing Inst Technol Southeast Acad Informat Technol Putian 351100 Peoples R China

ISBN: (纸本)9781665478960

With rapid development of Business-to-Customer (B2C) e-commerce, enormous goods assortment and fluctuated demand are presented in customer orders. Traditional manual warehouses have the demerits of low picking efficiency and high human cost, which misfit B2C e-commerce. To resolve the difficulties, a Robotic Mobile Fulfillment System (RMFS) is introduced and implemented. This paper studies the order sequencing problem in an RMFS with the situation that a rack can be reused among multiple picking stations in one rack schedule. In order to solve the problem, a Q-learning-based differential evolution algorithm is proposed. Compared with the existent algorithms, numerical experiments show that the proposed algorithm makes an evident improvement on order picking efficiency.

关键词： Robotic Mobile Fulfillment System order sequencing differential evolution algorithm Q-learning

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Multiple Topology SHADE with Tolerance-based Composite Framework for CEC2022 Single Objective Bound Constrained Numerical Optimization

Multiple Topology SHADE with Tolerance-based Composite Frame...

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IEEE Congress on evolutionary Computation (CEC)

作者： Sun, Bo Sun, Yafeng Li, Wei JiangXi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

To further enhance the convergence performance and accuracy of SHADE, a SHADE with tolerance-based multiple topology selection framework (MTT_SHADE) is proposed in this paper. In MTT_SHADE, three population topologies are established employing the k-nearest neighbor network, small-world network, and random network, respectively, and the evolution of individuals depends on the neighborhoods derived from different topologies. The tolerance-based composite framework is proposed to select the appropriate topology for an individual at the same time. Specifically, local tolerance and global tolerance are predetermined, corresponding to the tolerance for individuals and the population, respectively. The topology involved in the evolution of the individual is replaced when the individual does not progress after successive iterations. The population that does not improve in effect after successive iterations are considered to have exceeded the global tolerance and the three population topologies are reconstructed. The CEC2022 competition on single objective bound-constrained numerical optimization and four state-of-the-art DE variants are employed to investigate the effectiveness of the proposed algorithm. Experimental results show that MTT_SHADE is competitive in terms of accuracy and convergence.

关键词： differential evolution algorithm k-nearest neighbor small-world random network CEC2022

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Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

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DRONES 2024年第10期8卷 596-596页

作者： Li, Yonggang Wen, Dan Zhang, Siyuan Li, Longjiang Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Univ Elect Sci & Technol Sch Informat & Commun Engn Chengdu 611731 Peoples R China

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

关键词： multi-UAV artificial dragonfly swarms collaborative task allocation Dubins curves differential evolution algorithm

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Assessing digital transformation using fuzzy cognitive mapping supported by artificial intelligence techniques

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Erkan, Enes Furkan Uygun, Ozer Demir, Halil Ibrahim Sakarya Univ Ind Engn Dept TR-54000 Sakarya Turkiye

The digital transformation process aims to align people, machines, and technologies in interactive systems, prompting businesses to adapt their models for competitiveness. Understanding and implementing digital transformation becomes crucial for organizations to integrate physical and virtual systems throughout their operations. The study presents a comprehensive model emphasizing intelligent technology, organization, and manufacturing as essential criteria. Integrating fuzzy cognitive maps and fuzzy DEMATEL methods, the study captures complex relationships and employs a dynamic analysis involving three firms in the defense industry. Results showcase the companies' diverse digital transformation levels, assessed through scenarios and predictive analyses. Additionally, a sensitivity analysis underscores the impact of adjustments to the top three criteria, aiding businesses in deciding their transformational starting points. This integrated method, underpinned by fuzzy cognitive maps and artificial intelligence, emerges as a decision support system and a roadmap for enterprises navigating digital transformation.

关键词： differential evolution algorithm Digital transformation process Fuzzy cognitive maps Fuzzy DEMATEL

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XGBoost based solutions for detecting fraudulent credit card transactions

XGBoost based solutions for detecting fraudulent credit card...

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International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the Applications for Human Life

作者： El Kafhali, Said Tayebi, Mohammed Hassan First Univ Settat Fac Sci & Tech IR2M Lab Settat Morocco

ISBN: (纸本)9798350334449

Due to the emigration of businesses to the internet, credit cards have become a widely used tool for customers to pay for purchases both online and offline. However, fraudsters try to attack these payment tools using various techniques, and credit card fraud transactions have become dangerous. Credit card payment security is becoming an important research topic, and different ways are proposed to overcome this problem. In this article, we will propose a solution that combines the strengths of three methods to secure credit card fraud transactions. The differential evolution (DE) algorithm is used to select the best hyperparameters of the XGBoost algorithm (eXtreme Gradient Boosting). Synthetic Minority Oversampling (SMOTE) combined with Edited Nearest Neighbour (ENN) to handle the imbalanced data issues. Finally, we will classify fraud transactions using the optimized XGBoost algorithm. The proposed solution is evaluated using the European data set in terms of Accuracy, Recall, F1-score, and AUC. As a result, the proposed solution generally achieved the best score in terms of all the measurements used in this study. In terms of accuracy, we achieved 99.94%, similarly, we reached the best score in terms of the Precision score, which is 80.68%, in addition, we got 86.02% of recall score, 83.27 of F-measure, and 99.21 of AUC score. Thus, we emphasize the superiority of our proposed method over the competitive machine learning model utilized in this work.

关键词： Fraud Transactions Detection Hyperparameters Optimization differential evolution algorithm XGBoost algorithm Synthetic Minority Oversampling Edited Nearest Neighbor

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An improved artificial bee Colony algorithm based on cat mapping and differential variation

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Journal of Data, Information and Management 2022年第2期4卷 119-135页

作者： Zheng, Xuan Li, Xue Li, Yang Liu, Yu School of Electronic Control Chang’an University Shaanxi Province Xi’an 710064 China Xi’an Municipal Engineering Design & amp Research Institute Co. Ltd. Shaanxi Province Xi’an 710000 China

To overcome the problems that the traditional artificial bee colony algorithm easy to fall into local optimum and slow convergence rate, an improved artificial bee colony algorithm is proposed. The ergodicity of cat mapping is used for population initializing, and a chaotic search strategy improved by cat mapping is designed in the mining bee stage for population diversity increasing. And then, to avoid the algorithm falling into a local optimum, differential mutation is used for improving the search strategy, and adaptive scaling factor is designed for achieving dynamic search. The simulation results of 10 benchmark functions compared with the ABC algorithm, SATC-ABC algorithm, CABC algorithm, MSSABC algorithm and GABC algorithm were performed. The proposed algorithm in this paper can improve the optimization performance effectively, and ability of jump out of the local optimum increasing. At the same time, improve the convergence speed, and obtain the optimal function value. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.

关键词： Adaptive Artificial bee colony Cat mapping differential evolution algorithm Search mechanism

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Applying a Novel iDEaSm with Maintaining Diversity to Wind Turbine Design Optimization Problem 12

Applying a Novel iDEaSm with Maintaining Diversity to Wind T...

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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Hori, Takato Uchitane, Takeshi Ito, Nobuhiro Iwata, Kazunori Aichi Inst Technol Grad Sch Business Adm & Comp Sci Toyota Japan Aichi Inst Technol Fac Informat Sci Toyota Japan Aichi Univ Fac Business Adm Nagoya Aichi Japan

ISBN: (纸本)9781665499248

Wind Turbine Design Optimization Problem (WTDOP) is a real-world optimization problem. The WTDOP is also the problem of the evolutionary Computation Competition 2019 organized by the Japanese Society of evolutionary Computation. The WTDOP as a single-objective optimization problem has one known characteristic and two reported characteristics. The first characteristic is that the constraint satisfaction of a solution candidate can be obtained only when the solution candidate is evaluated. The second characteristic is that constraint violations occur at a high rate. The third characteristic is that the WTDOP has a multimodal fitness landscape. We propose three improvements to the existing method (new Evaluation Function, Distance Limit, and Deleted Individuals). We compared the results of the numerical experiment based on the median of 21 experiments according to the rules of the competition. The fitness function value of the best solution, the mean, and the median were all better than the existing method. Moreover, in the best result of all trials, diversity was maintained until the final generation.

关键词： iDEaSm differential evolution algorithm multimodal optimization problem evolutionary computation competition surrogate model

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An efficient two-stage optimization algorithm for a flexible job shop scheduling problem with worker shift arrangement

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COMPUTERS & OPERATIONS RESEARCH 2024年 171卷

作者： Li, Hui Peng, Jianbiao Wang, Xi Cent Univ Finance & Econ Sch Informat Beijing Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Cent Univ Finance & Econ Bldg 7Shunsha Rd Beijing Peoples R China Cent South Univ Comp Bldg Changsha Hunan Peoples R China

Due to the involvement of workers, scheduling production jobs necessitates consideration of worker shifts in most production activities. In this study, we address a flexible job shop scheduling problem with worker shift arrangement, considering constraints such as job priority, limited resources, and resource unavailability. To minimize the overdue days of low-priority jobs and ensure the timely delivery of high-priority jobs, we establish a mixed-integer programming model to allocate production resources, process sequencing, and schedule worker shifts. An improved differential evolution algorithm is proposed and designed such that overdue days and worker overtime of all jobs are calculated. Furthermore, we develop a two-stage intelligent optimization algorithm. First, we design a two-segment chromosome encoding and decoding method. Then, we propose generation strategies that follow the urgency of the priority rule to generate high-quality initial chromosomes. In adaptive worker shift adjustment, we prioritize high-priority jobs to align with delivery times. We conducted experiments to validate our model and algorithm by comparing them against four well-known intelligent optimization algorithms. Our improved algorithm proves to be highly beneficial in job and worker scheduling as it effectively minimizes overdue days and arranges worker overtime.

关键词： Flexible job shop scheduling Job priority differential evolution algorithm Resource unavailability Worker shift arrangement

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Classification of Ultrasound Breast Images Using Fused Ensemble of Deep Learning Classifiers 17

Classification of Ultrasound Breast Images Using Fused Ensem...

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17th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA)

作者： Nehary, E. A. Rajan, Sreeraman Carleton Univ Syst & Comp Engn Ottawa ON Canada

ISBN: (数字)9781665482998

ISBN: (纸本)9781665482998

Ultrasound (US) imaging is an affordable, radiation free screening that has been successfully used for early stage breast cancer screening. Deep learning-based classifiers are currently being used to classify breast cancer. Deep learning requires large amount of dataset for training. However, currently available databases of breast cancer US images are small and the images have tumors of different sizes. Therefore, the deep learning based classifiers are unable to provide good generalization. To address these challenges, we propose a fusion of three models namely transfer learning, multi-scale and autoencoder. Transfer learning model is based on VGG16 and is used to overcome the issue of limited data. Convolutional autoencoders extract features that can represent even noisy images. We propose a novel multi-scale deep learning model to address learning of US images with tumors of various sizes and shapes. These three models are trained independently and then their classification outputs are fused using differential evolution (DE) algorithm to get the final classification results. The proposed novel fused ensemble of deep learning-based classifiers is evaluated using two publicly available US datasets. Transfer learning, autoencoder, and multi-scale models individually achieve an accuracy of 88%, 85%, and 89% respectively. The fusion of the outputs of the three models using DE algorithm provides a classification accuracy with an accuracy of 93%. The source code available at https://***/EbrahimAli1989/Breast-Cancer-classification-.git.

关键词： Ultrasound imaging CNN autoencoder transfer learning differential evolution algorithm Breast cancer Fusion

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