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A fixed point evolution algorithm based on expanded Aitken rapid iteration method for global numeric optimization

MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 229卷 288-303页

作者： Zhang, Qian Hu, Zhongbo Hong, Nan Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou 434000 Hubei Peoples R China

evolution algorithms based on mathematical models whose reproduction operators are derived from mathematical models are a promising branch of metaheuristic algorithms. Aitken rapid iteration method, as a fixed point iteration technique for solving nonlinear equations, performs a procedure of progressive display of a root and generates an iterative sequence that exhibits a convergent trend. Inspired by the idea that an iterative sequence gradually converges to the optimal point during the progressive display procedure of a fixed point of an equation, a fixed point evolution algorithm based on the expanded Aitken rapid iteration method (FPEea) is proposed. To develop FPEea, an expanded Aitken rapid model is first constructed. Then, three polynomials which are derived from the expanded Aitken rapid model are used as the reproduction operator of FPEea to produce offspring. The performance of FPEea is investigated on CEC2019 and CEC2020 benchmark function sets, as well as four engineering design problems. Experimental results show that FPEea is an effective and competitive algorithm compared with several classical evolution algorithms and state-of-the-art algorithms.

关键词： Expanded Aitken rapid iteration method evolution algorithms Fixed point iterative method Mathematical models

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A subspace strategy based coevolutionary framework for constrained multimodal multiobjective optimization problems

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SWARM AND evolutionARY COMPUTATION 2025年 95卷

作者： Yan, Li Guo, Shunge Liang, Jing Qu, Boyang Li, Chao Yu, Kunjie Zhongyuan Univ Technol Sch Automat & Elect Engn Zhengzhou 450007 Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

Constrained multimodal multiobjective optimization problems (CMMOPs) consist of multiple equivalent constrained Pareto sets (CPSs) that have the identical constrained Pareto front (CPF). The key to solving CMMOPs lies in how to locate and retain CPSs and CPF in search spaces. Thus, this paper proposes a subspace strategy based coevolutionary framework for CMMOPs, named SCCMMO. Firstly, the subspace generation and maintenance strategy is proposed to efficiently locate multiple CPSs within the decision space. Secondly, the subspace-type perception strategy is used to exploit the feasible and infeasible information in subspaces. Finally, a coevolutionary framework is introduced to improve search efficiency. To prove the effectiveness of the algorithm, the proposed method is compared with ten state-of-the-art algorithms on seventeen benchmarks. The results demonstrate the superiority of SCCMMO in solving CMMOPs. Moreover, SCCMMO also achieves better performance on the real-world problem.

关键词： Constrained multimodal multiobjective optimization Subspace strategy Coevolution evolution algorithms

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IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model

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FUTURE INTERNET 2025年第2期17卷 56-56页

作者： Quoc, Huu Dang The, Loc Nguyen Quang, Truong Bui Minh, Phuong Han Thuongmai Univ Fac Econ Informat Syst & Ecommerce 79 Ho Tung Mau Hanoi 100000 Vietnam Hanoi Natl Univ Educ Fac Informat Technol 136 Xuan Thuy Hanoi 100000 Vietnam

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company's MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.

关键词： IoT application IoT in sewing production scheduling problem evolution algorithms intelligent planning production planning Real-RCPSP problem

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Optimization of Holding Force for a Climbing Robot Based on a Differential evolutionary Algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2024年第1期17卷 54-59页

作者： Masike, Rujeko Kaur, Karamjit Arora, Rajesh Shridhara, Somalapura Nagappa Amity Univ Haryana Amity Sch Engn & Technol Dept ECE Panchgaon India HITS Univ Hindustan Inst Technol & Sci Deemed Univ Chennai India Amity Univ Haryana Amity Sch Engn & Technol Panchgaon India

Background: The advancements in robotic technology have completely revolutionized day-to-day life. In industrial applications, the implementation of robotics is quite advantageous as it may help in performing dangerous tasks like climbing high walls, working in a high-temperature environment, high radiation exposure conditions ***;This paper presents the design and development of a wall-climbing robot for dam wall inspection using an adaptive aerodynamic adhesion technique. The optimization of a robot design is done using a differential evolutionary ***: In the proposed model, the principle of Bernoulli adhesion is used for designing the suction pad. The optimization of various variables is done using a differential evolutionary algorithm to improve the efficiency and effectiveness of the wall climbing robot ***: The results of the proposed system show that the approach can find an optimal holding force and can be effectively used for applications like dam wall climbing for inspection.

关键词： Bernoulli pad wall-climbing robot evolution algorithms adaptive holding force dynamic variations variable optimization climbing robot

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A Novel Energy-Efficient MIMO-OFDM Decoder Architecture with Error Detection

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IETE JOURNAL OF RESEARCH 2023年第4期69卷 1974-1984页

作者： Preethi, D. Valarmathi, R. S. Bannari Amman Inst Technol Dept Elect & Commun Engn Erode Tamil Nadu India

Medical data transmission is a major challenge in wireless communication to preserve their integrity and coherence. As the transmission takes place at high data rate, the complexity and throughput of the system, pertaining to the fetal ECG signal, is crucial. In addition to the accumulation of noises in the channel, the observation of fetal ECG signal, using diagnostic devices, also provides additive noise, thereby reducing the quality of the transmitted signal. The major motive of this paper is to demarcate the fetal ECG signal from noise at early stages of transmission using Chambolle-Pock based hybridized classifiers. This technique provides sensitivity of 91.1% followed by performance validation of Error Detection and correction adders for different decoder architectures in transceiver. Thereby area and power reduction of 86.3% and 91.1% is achieved. The simulations are carried out with MIT-BIH Arrhythmia using simulation tools, such as MATLAB 2013b, ModelSim 10.0b, and Cadence Virtuoso under 90 nm on over 200 data sets.

关键词： Additive noises Decoder architectures Error detection and correction adders evolution algorithms

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Chaotic attractors of discrete dynamical systems used in the core of evolutionary algorithms: state of art and perspectives

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JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS 2023年第9-12期29卷 1202-1227页

作者： Zelinka, Ivan Senkerik, Roman VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic Tomas Bata Univ Zlin Fac Appl Informat AI Lab Zlin Czech Republic Tomas Bata Univ Zlin Fac Appl Informat AI Lab TG Masaryka 5555 Zlin 76001 Czech Republic

This paper delves into the intersection between discrete dynamical systems and bio-inspired metaheuristic algorithms. Chaos, an inherent phenomenon manifested by both continuous and discrete dynamic nonlinear systems has become an essential component of engineering design. This study examines the relationship between chaos and bio-inspired metaheuristic algorithms from two distinct angles: the presence of chaos within the realm of algorithms, and the application of bio-inspired algorithms for the identification, control, or synthesis of complex systems. Furthermore, this paper offers an in-depth exploration of the interplay between metaheuristics and complex nonlinear dynamics, including potential avenues for future research. The presented simulation studies, along with the design of objective functions for optimization and the implementation of metaheuristics, serve as a valuable foundation for subsequent experiments involving discrete nonlinear systems and complex systems with time delays. Such systems may benefit from multiparametric or multicriteria optimization approaches, paving the way for novel advancements in the field.

关键词： Deterministic chaos metaheuristics evolution algorithms swarm intelligence discrete chaotic dynamical systems chaotic maps

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BESS: Balanced evolutionary semi-stacking for disease detection using partially labeled imbalanced data

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INFORMATION SCIENCES 2022年 594卷 233-248页

作者： Ning, Zhihan Ye, Ziqing Jiang, Zhixing Zhang, David Chinese Univ Hongkong Shenzhen Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

Machine learning offers automatic and objective approaches for disease detection based on biomedical data. However, 1) the percentage of patients in the real world is smaller than that of healthy people;2) the annotations by medical experts are costly. Therefore, medical datasets are often imbalanced and partially labeled. To address this problem, we propose balanced evolutionary semi-stacking (BESS) for disease detection using partially labeled imbalanced (PLI) data. BESS aims to detect illnesses by considering the input of the color, texture, and geometry features extracted from tongue images. Specifically, the proposed method first mitigates the class imbalance problem and leverages the unlabeled data through the so-called balanced evolutionary co-training approach. Then BESS exploits both the data and classifier diversity obtained by balanced evolutionary co-training to improve the performance of the stacking ensemble. We quantitatively evaluate the proposed algorithm based on the PLI tongue image database. BESS achieves the best performance in detecting diabetes mellitus, chronic kidney disease, breast cancer, and chronic gastritis, compared to other state-of-the-art methods. The results of the experiments substantiate the superiority and effectiveness of the proposed algorithm. Codes and datasets have been made publicly available at url: https://***/CUHKSZ-NING/Balanced-evolutionary-Semi-Stacking. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Disease detection Semi-supervised learning Imbalanced data Tongue features evolution algorithms

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Testing and Verification of the Deep Neural Networks Against Sparse Pixel Defects 1

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41st International Conference on Computer Safety, Reliability and Security (SAFECOMP)

作者： Szczepankiewicz, Michal Radlak, Krystian Szczepankiewicz, Karolina Popowicz, Adam Zawistowski, Pawel NVIDIA Warsaw Poland Warsaw Univ Technol Warsaw Poland Vay Technol Berlin Germany Silesian Tech Univ Gliwice Poland

ISBN: (数字)9783031148620

ISBN: (纸本)9783031148620;9783031148613

Deep neural networks can produce outstanding results when applied to image recognition tasks but are susceptible to image defects and modifications. Substantial degradation of the image can be detected by automatic or interactive prevention techniques. However, sparse pixel defects may have a significant impact on the dependability of safety-critical systems, especially autonomous driving vehicles. Such perturbations can limit the perception capabilities of the system while remaining undetected by human observer. The effective generation of such cases facilitates the simulation of real-life challenges caused by sparse pixel defects, like occluded or stained objects. This work introduces a novel sparse adversarial attack generation method based on differential evolution strategy. Additionally, we introduce a novel framework for sparse adversarial attack generation, which can be integrated into the safety-critical systems development process. An empirical evaluation demonstrates that the proposed method outperforms and complements state-of-the-art techniques allowing for complete evaluation of an image recognition system.

关键词： Dependability Adversarial attacks Deep learning evolution algorithms Differential evolution

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Optimal brick layout of masonry walls based on intelligent evolutionary algorithm and building information modeling

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AUTOMATION IN CONSTRUCTION 2021年 129卷 103824-103824页

作者： Xu, Chengran Liu, Jiepeng Li, Sheng Wu, Zhou Chen, Y. Frank Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA

The Masonry wall (MW) is an important element of buildings. Currently, the detailed brick layout of a nonmodular MW mostly relies on the experience from a bricklayer. The manual building information modeling (BIM) technique can facilitate the modeling of bricks, but is error-prone and time-consuming. This paper presents a BIM-based framework to generate the layouts and brick take-offs automatically. A computation module is developed to find the optimal brick layout using the three-stage optimization. For a MW, the first-stage determines the vertical position of each course and the second and third stages obtain the horizontal positions and sizes of bricks for odd and even courses respectively. The intelligent algorithm neighborhood field optimization is applied to the computation module. The proposed framework is applied in the automated detailing design of MWs with different bonds in two rooms to demonstrate its practicability and efficiency.

关键词： Masonry walls (MWs) Detailing design Brick layout evolution algorithms Building information modeling (BIM)

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Automated steel reinforcement detailing in reinforced concrete frames using evolutionary optimization and artificial potential field

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AUTOMATION IN CONSTRUCTION 2022年 138卷 104224-104224页

作者： Xu, Chengran Liu, Jiepeng Wu, Zhou Chen, Y. Frank Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Automat 174 Shazheng St Chongqing 400044 Peoples R China Penn State Univ Dept Civil Engn Middletown PA 17057 USA

Steel reinforcement detailing is critical in the reinforced concrete (RC) frame design of collision-free rebar layouts. This paper presents a methodological framework for automated steel reinforcement detailing. An optimization model is developed to calculate optimal rebar combinations, where material cost and construction convenience are considered. The neighborhood field optimization (NFO) algorithm is applied to obtain the solution, and an integrated artificial potential field (APF) and NFO method is proposed to automatically generate a collision-free rebar layout, where the NFO enables agents to escape from the local minimum point of the APF. The developed framework is applied to two RC frames to determine the diameter, length, and position of each rebar. The results show that the developed framework can rapidly and accurately complete steel reinforcement detailing for various RC frames.

关键词： Reinforced concrete (RC) frames Steel reinforcement Rebar combination Rebar layout evolution algorithms Artificial potential field (APF)

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