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Fast Reverse Design of 4D-Printed Voxelized Composite Structures Using Deep Learning and evolutionary algorithm

ADVANCED SCIENCE 2025年第12期12卷 e2407825页

作者： Wang, Mengtao Liu, Zaiyang Furukawa, Hidemitsu Li, Zhuo Ge, Yifei Xu, Yifan Qiu, Zhe Tian, Yang Wang, Zhongkui Xu, Ren Meng, Lin Ritsumeikan Univ Dept Elect & Comp Engn Shiga 5258577 Japan Ritsumeikan Univ Dept Robot Shiga 5258577 Japan Yamagata Univ Dept Mech Syst Engn Yonezawa 9928510 Japan Xiamen Univ Sch Med Xiamen 361100 Fujian Peoples R China

Designing voxelized composite structures via 4D printing involves creating voxel units with distinct material properties that transform in response to stimuli;however, optimally distributing these properties to achieve specific target shapes remains a significant challenge. This study introduces an optimization method combining deep learning (DL) and an evolutionary algorithm, focusing on a solvent-responsive hydrogel as the target material. A sequence-enhanced parallel convolutional neural network is developed and generated a dataset through finite element simulations. This DL model enables high-precision prediction of hydrogel deformation. Furthermore, a progressive evolutionary algorithm (PEA) is proposed by integrating the DL model to construct a DL-PEA framework. This framework supports rapid reverse engineering of the desired shape, and the average design time for specified target shapes is reduced to approximate to 3.04 s. The present findings illustrate how 4D printing of optimized hydrogel designs can effectively transform in response to environmental stimuli. This work provides a new perspective on the application of hydrogels in 4D printing and presents an efficient tool for optimizing 4D-printed voxelized composite structures.

关键词： 4D printing deep learning evolutionary algorithm hydrogel voxel assembly

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Community Detection method based on Random walk and Multi objective evolutionary algorithm in complex networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2025年 234卷

作者： Dabaghi-Zarandi, Fahimeh Afkhami, Mohammad Mehdi Ashoori, Mohammad Hosein Vali E Asr Univ Rafsanjan Comp Engn Dept Rafsanjan Iran

In recent years, due to the existence of intricate interactions between multiple entities in complex networks, ranging from biology to social or economic networks, community detection has helped us to better understand these networks. In fact, research in community detection aims at extracting several almost separate sub-networks called communities from the complex structure of a network in order to gain a better understanding of network topology and functionality. In this regard, we propose a novel community detection method in this paper that is performed based on our defined architecture composed of four components including Pre-Processing, Primary Communities Composing, Population Generating, and Genetic Mutation components. In the first component, we identify and store similarity measures and estimate the number of communities. The second component composes primary community structures based on several random walks from significant center nodes. Afterwards, our identified primary community structure is converted to a suitable chromosome structure to use in next evolutionary-based components. In the third component, we generate a primary population along with their objective function. Then, we select several significant chromosomes from the primary population and merge their communities in order to generate subsequent populations. Finally, in the fourth component, we extract several best chromosomes and apply the mutation process on them to reach the best community structure considering evaluation functions. We evaluate our proposal based on different size of network scenarios including both real and artificial network scenarios. Compared with other approaches, the community structures detected by our proposal are not dependent on the size of networks and exhibit acceptable evaluation measures in all types of networks. Therefore, our proposal can detect results similar to real community structure.

关键词： Community detection Random walk algorithm Complex networks Similarity based on graph structure evolutionary algorithm

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An efficient problem-specific evolutionary algorithm for flexible job shop scheduling problem with specific workers in highly customised manufacturing systems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Li, Jiahang Liu, Qihao Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan Peoples R China

The integrated optimisation problem of production scheduling and workforce scheduling has emerged as a critical challenge in modern manufacturing systems. Although existing research predominantly addresses single-tasking workers capable of handling one operation at a time, the scheduling complexity introduced by multitasking workers performing multiple operations at a time remains understudied. This gap is particularly significant in highly customised industries such as shipbuilding and aerospace manufacturing, where the versatility of the workforce substantially impacts production efficiency. This study investigates the flexible job-shop scheduling problem with multitasking workers (FJSP-MW), proposing a genetic algorithm with knowledge-based local search (GALS). The proposed algorithm incorporates two key innovations: (1) a disjunctive graph model for FJSP-MW with a total weighted tardiness (TWT) objective function and (2) problem-specific neighbourhood structures based on critical paths. Comprehensive experiments evaluate the algorithm's performance using 20 instances and a case study. The results of the case study demonstrate significant improvements;reductions of 32.14% in TWT and 39.02% in makespan are obtained compared to the original scheduling solution. The results confirm that GALS outperforms state-of-the-art algorithms in solution quality and convergence speed.

关键词： Flexible job-shop scheduling multitasking workers workforce scheduling evolutionary algorithm genetic algorithm

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Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data

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BRIEFINGS IN BIOINFORMATICS 2025年第1期26卷 bbae681页

作者： Galindo-Hernandez, Raul Rodriguez-Vazquez, Katya Galan-Vasquez, Edgardo Castellanos, Carlos Ignacio Hernandez Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Circuito Escolar Ciudad Univ Mexico City 04510 Mexico

Analyzing gene expression data helps the identification of significant biological relationships in genes. With a growing number of open biological datasets available, it is paramount to use reliable and innovative methods to perform in-depth analyses of biological data and ensure that informed decisions are made based on accurate information. evolutionary algorithms have been successful in the analysis of biological datasets. However, there is still room for improvement, and further analysis should be conducted. In this work, we propose Online-Adjusted evolutionary Biclustering algorithm (OAEVOB), a novel evolutionary-based biclustering algorithm that efficiently handles vast gene expression data. OAEVOB incorporates an online-adjustment feature that efficiently identifies significant groups by updating the mutation probability and crossover parameters. We utilize measurements such as Pearson correlation, distance correlation, biweight midcorrelation, and mutual information to assess the similarity of genes in the biclusters. algorithms in the specialized literature do not address generalization to diverse gene expression sources. Therefore, to evaluate OAEVOB's performance, we analyzed six gene expression datasets obtained from diverse sequencing data sources, specifically Deoxyribonucleic Acid microarray, Ribonucleic Acid (RNA) sequencing, and single-cell RNA sequencing, which are subject to a thorough examination. OAEVOB identified significant broad gene expression biclusters with correlations greater than $0.5$ across all similarity measurements employed. Additionally, when biclusters are evaluated by functional enrichment analysis, they exhibit biological functions, suggesting that OAEVOB effectively identifies biclusters with specific cancer and tissue-related genes in the analyzed datasets. We compared the OAEVOB's performance with state-of-the-art methods and outperformed them showing robustness to noise, overlapping, sequencing data sources, and gene covera

关键词： biclustering evolutionary algorithm gene expression data RNA-sequencing single-cell RNA-sequencing

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evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation

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INFORMATION SCIENCES 2013年 233卷 214-229页

作者： Horng, Shih-Cheng Lin, Shin-Yeu Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan

This work proposes an evolutionary algorithm (EA) that is assisted by a surrogate model in the framework of ordinal optimization (OO) and optimal computing budget allocation (OCBA) for use in solving the real-time combinatorial stochastic simulation optimization problem with a huge discrete solution space. For real-time applications, an off-line trained artificial neural network (ANN) is utilized as the surrogate model. EA, assisted by the trained ANN, is applied to the problem of interest to obtain a subset of good enough solutions, S. Also for real-time application, the OCBA technique is used to find the best solution in S, and this is the obtained good enough solution. Most importantly, a systematic procedure is provided for evaluating the performance of the proposed algorithm by estimating the distance of the obtained good enough solution from the optimal solution. The proposed algorithm is applied to a hotel booking limit (HBL) problem, which is a combinatorial stochastic simulation optimization problem. Extensive simulations are performed to demonstrate the computational efficiency of the proposed algorithm and the systematic performance evaluation procedure is applied to the HBL problem to quantify the goodness of the obtained good enough solution. (C) 2013 Elsevier Inc. All rights reserved.

关键词： evolutionary algorithm Ordinal optimization Optimal computing budget allocation Artificial neural network Stochastic simulation Combinatorial optimization

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evolutionary algorithm for Software Project Scheduling Considering Team Relationships

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IEEE ACCESS 2023年 11卷 43690-43706页

作者： Zhang, Jianhao Shen, Xiaoning Yao, Chengbin Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Jiangsu Peoples R China Jiangsu Key Lab Big Data Anal Technol Nanjing 210044 Jiangsu Peoples R China Jiangsu Engn Res Ctr Meteorol Energy Using & Contr Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

Human relationships have a great impact on the software development process. Meanwhile, project development may require the acquisition of new skills that employees have not yet acquired due to changes in customer requirements. However, such factors are seldom incorporated into software project scheduling problem. To address this, a novel mathematical model considering the team relationships is established for software project scheduling problem. This model introduces the communication cost factor of each employee, and analyses its quantitative connections with changes in human relationships and the growth of skill proficiency respectively. In addition, a selection mechanism for external employees is designed to meet new skill requirements. To solve the model, an improved bi-population discrete evolutionary algorithm based on information feedback is proposed. The heuristic information of "employee suitability" is utilized in initialization to obtain better initial individuals. The feedback mechanism based on "evolutionary quality" is applied to provide an effective strategy for adaptive tuning of subpopulation size. The selection probabilities of distinct crossover operators are adjusted according to the "improved quantity" to increase crossover efficiency. Moreover, an enhanced local search strategy based on the "degree of team cooperation" and the "improved quantity" is developed. Experimental results show that by considering human relationships under different communication cost factors, the duration and cost of the project are significantly reduced. Compared with four state-of-the-art algorithms, the scheduling performance of the proposed algorithm can be improved by 10.6374% to 25.8566%.

关键词： Software Task analysis Costs Software algorithms evolutionary computation Optimization Optimal scheduling Software project scheduling communication cost human relationship information feedback evolutionary algorithm

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evolutionary algorithm for advanced process planning and scheduling in a multi-plant

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COMPUTERS & INDUSTRIAL ENGINEERING 2005年第2期48卷 311-325页

作者： Moon, C Seo, Y Korea Univ Dept Ind & Informat Engn Seoul 136701 South Korea Hanyang Univ Dept Informat & Ind Engn Ansan 425791 South Korea

Integration of process planning and scheduling is one of the most important functions to support flexible planning in a multi-plant. The planning and scheduling are actually interrelated and should be solved simultaneously. In this paper, we propose an advanced process planning and scheduling model for the multi-plant. The objective of the model is to decide the schedules for minimizing makespan and operation sequences with machine selections considering precedence constraints, flexible sequences, and alternative machines. The problem is formulated as a mathematical model, and an evolutionary algorithm is developed to solve the model. Numerous experiments arc carried out to demonstrate the efficiency of the proposed approach. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： process planning and scheduling multi-plant chain TSP with precedence constraint evolutionary algorithm

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evolutionary algorithm based structure search and first-principles study of B₁₂C₃ polytypes

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JOURNAL OF ALLOYS AND COMPOUNDS 2017年 695卷 2023-2034页

作者： Pillai, Harikrishnan Gopalakrishna Madam, Ajith Kulangara Chandra, Sharat Cheruvalath, Valsakumar Mundachali NITK Dept Phys Surathkal 575025 Karnataka India IGCAR Div Mat Phys Kalpakkam 603102 Tamil Nadu India Indian Inst Technol IIT Palakkad Dept Phys Palakkad 678557 Kerala India

The structure search based on evolutionary algorithm has yielded six unique Boron Carbide structures in B12C3 stoichiometry, three of them with negative formation energies. Their formation energies lie within a band of 166 meV/atom, so they can be formed together in any optimal high temperature synthesis of B12C3 and they are thermodynamically stable at temperatures up to 660 K. This work is the first independent confirmation using structure search that B11Cp(CBC) is the ground state structure of B12C3 stoichiometry. New structures like the 14-atom-cage and the supercell (B11Cp)(B10C2pe)(CBC)(CBB) have also emerged in the structure search. Five structures have base-centered monoclinic symmetry and the supercell has triclinic symmetry, implying that the determination of monoclinic symmetry in B12C3 by experimental measurements is an option for further inquiry. The mechanical stability of these systems are established through the analysis of their elastic constants and their dynamical stability from the phonon data. The high value of Bulk modulus (similar to 250 GPa) indicates their high hardness and the B/G value confirms their brittle nature. The electronic structure shows that they are semiconductors with a significant reduction in the band gap when the structure does not contain the CBC chain. The curve fitting of the cumulative IR spectrum against the experimental spectrum implies that the presence of B11Cp(CBC) in the ground state composition could mostly be through structures of larger unit cells. The hardness values of these systems estimated by using the semi-empirical model based on bond strength are in excellent agreement with the experimental values. For the four structures with chain the hardness values are close to the superhard regime (>40 GPa). (C) 2016 Elsevier B.V. All rights reserved.

关键词： Boron Carbides Density functional theory evolutionary algorithm Hardness

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evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

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KNOWLEDGE-BASED SYSTEMS 2013年第Feb.期39卷 9-22页

作者： Mookiah, M. R. K. Acharya, U. Rajendra Martis, Roshan Joy Chua, Chua Kuang Lim, C. M. Ng, E. Y. K. Laude, Augustinus Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur Malaysia Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Tan Tack Seng Hosp Inst Eye Natl Healthcare Grp Singapore Singapore

Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p < 0.0001) features for Probabilistic Neural Network (PNN), Decision Tree (DT) C4.5, and Support Vector Machine (SVM) to select the best classifier. The best model parameter (sigma) for which the PNN classifier performed best was identified using global optimization techniques such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). We demonstrated an average classification accuracy of 96.15%, sensitivity of 96.27% and specificity of 96.08% for sigma = 0.0104 using threefold cross validation using PNN classifier. The computer-aided diagnosis (CAD) results were validated by comparing with expert ophthalmologists. The proposed automated system can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Diabetic retinopathy Bifurcation point Local binary pattern Laws texture energy evolutionary algorithm

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evolutionary algorithm-based fuzzy PD control of spillway gates of dams

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2007年第8期344卷 1039-1055页

作者： Bagis, Aytekin Karaboga, Dervis Erciyes Univ Dept Elect Elect Engn TR-38039 Kayseri Turkey Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey

In this paper, an evolutionary algorithm ( EA)-based fuzzy proportional-derivative (PD)-type controller is employed to reservoir control of dams with the purpose of operating spillway gates during any flood of any magnitude, which is not predictable beforehand. EA is used to evolve the main parameters of the fuzzy PD controller. The use of the EA, in conjunction with a systematic neighborhood structure for the determining of fuzzy rule-base parameters, leads to a significant improvement in the performance of the controller. The major objective of the controller is to achieve better system performance over the conventional control methods. In order to demonstrate the high performance of the presented method, we simulate the control system using different probable inflow hydrographs of various magnitudes. The simulation results indicate that the EA-based fuzzy PD controller not only performs an accurate and efficient solution, but also exhibits more desirable and reliable results than the conventional approaches. (c) 2007 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： fuzzy PD control evolutionary algorithm spillway gate reservoir operation

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