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nested genetic algorithm-based classifier selection and placement in multi-level ensemble framework for effective disease diagnosis

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年第4期28卷 487-510页

作者： Arukonda, Srinivas Cheruku, Ramalingaswamy Natl Inst Technol Warangal Dept Comp Sci Engn Hanamkonda India

Effective disease diagnosis is a critical unmet need on a global scale. The intricacies of the numerous disease mechanisms and underlying symptoms make developing a model for early diagnosis and effective treatment extremely difficult. Machine learning (ML) can help to solve some of these issues. Recently, various ensemble-based ML models have benefited clinicians in early diagnosis. However, one of the most difficult challenges in multi-level ensemble approaches is the classifier selection and their placement in the ensemble framework as it improves the overall performance. Let m classifiers have to select from n classifiers there are ((n)(m)) ways. Again, these ((n)(m)) possibilities can be arranged in m! ways. Finding the best m classifiers and their positions from total ((n)(m))m! ways is a challenging and hard problem. To address this challenge, a dynamic three-level ensemble framework is proposed. A nested genetic algorithm (GA) and ensemble-based fitness function are employed to optimize the classifier selection and their placement in a three-level ensemble framework. Our approach used eleven classifiers and chose seven classifiers by maximizing the fitness function. The proposed model experiments on 12 disease datasets. The proposed model outperformed in terms of accuracy, F1, and G-measure on the Chronic Kidney Disease (CKD) dataset is 0.987, 0.988, and 0.989, respectively. In terms of AUC on the Heart disease dataset (HDD) is 0.998 and in terms of recall on the Hypothyroid disease dataset (HyDD) is 0.988. In addition, the proposed model superiority is statically evaluated by Wilcoxon-Signed-Rank (WSR) test compared with other ensemble models, such as random forest (RF), bagging classifier (BC), XGBoost (XGB), and gradient boost classifier (GBC) with probability value p < 0.05 results shows all the traditional ensemble model differs with proposed model and also effective size evaluated with using the matched-pairs rank biserial correlation coefficient wc an

关键词： Disease diagnosis nested genetic algorithm evolutionary algorithm multi-level ensemble framework classifier placement cross validation

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nested genetic algorithm for highly reliable and efficient embedded system design

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DESIGN AUTOMATION FOR EMBEDDED SYSTEMS 2020年第4期24卷 185-221页

作者： Israr, Adeel Kaleem, Mohammad Nazir, Sajid Mirza, Hamid Turab Huss, Sorin Alexander COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad Campus Islamabad Pakistan Glasgow Caledonian Univ Glasgow Lanark Scotland COMSATS Univ Islamabad Lahore Campus Lahore Pakistan Tech Univ Darmstadt Darmstadt Germany

Modern embedded systems must have high reliability and performance. They should be able to tolerate both hard as well as soft errors occurring in the resources constituting the system. Reliability must be part of the system design and the system must consist of non expensive off-the-shelf resources. A system-level design process of reliable system demands efficient reliability evaluation of the explored design alternatives (DA). This work presents a new approach to accelerate the calculation of reliability and execution time of the system and thereby suggests the design space exploration for a reliable system. A new data structure denoted as system error decision diagram (SEDD) is proposed, which is based on both binary decision diagrams to model hard errors and zero-suppressed decision diagrams to model soft errors. The construction of the SEDD diagram and the calculation of reliability and execution time are explained in an algorithmic way. SEDD is found to be better in terms of memory requirements and construction time compared to other models available in the literature. Using SEDD and the corresponding algorithms, a nested genetic algorithm is constructed that designs system for lifetime reliability and execution time. The result of the design space exploration algorithm is a set of Pareto DAs. A so-called 'human designer' is thus able to select one of the best alternative that represents the given system requirements. The nested genetic algorithm and its benefits are illustrated using a real-life embedded application from automotive domain.

关键词： nested genetic algorithm Reliability Execution time Decision diagram

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A bi-level optimisation approach for assembly line design using a nested genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第24期59卷 7560-7575页

作者： Leiber, Daria Reinhart, Gunther Tech Univ Munich Inst Machine Tools & Ind Management Dept Mech Engn Munich Germany

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually.

关键词： assembly line balancing assembly line design assembly line planning layout planning nested genetic algorithm bi-level optimisation

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Joint production planning, pricing and retailer selection with emission control based on Stackelberg game and nested genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 161卷 113733-113733页

作者： Zhang, Linda L. Gang, D. U. Jun, W. U. Yujie, M. A. IESEG Sch Management 1 Parvis La Def F-92044 Paris France Univ Lille LEM CNRS 9221 F-59655 Villeneuve Dascq France Tianjin Univ Coll Management & Econ 92 Weijin Rd Tianjin 300072 Peoples R China

In practice, it is of paramount importance that firms make joint decisions in production planning, pricing and retailer selection while considering emission regulation. This is because the joint decisions can ensure firms to obtain higher profits while contributing to sustainable environments. However, due to the problem complexity, no models facilitating such decision making are available. This study aims to develop a model to help firms make optimal joint decisions. To model the situations where a manufacturer is the leader and the retailers are followers, we adopt the Stackelberg game theory and develop a 0- 1 mixed nonlinear bilevel program to maximize the profits of both the manufacturer and his retailers. We further develop a nested genetic algorithm to solve the game model. Numerical examples demonstrate (i) the applicability of the game model and the algorithm and (ii) the robustness of the algorithm. Managerial insights are obtained, suggesting that (i) manufacturers need to identify the capacity ranges (called capacity traps) where capacity increases result in reduced profits when making decisions to optimize profits;(ii) retailers should make suitable, e.g., pricing decisions so that the manufacturers can include them in the supply chains;(iii) both manufacturers and retailers may not need to consider the carbon emission buying (or selling) price when making decisions. (c) 2020 Published by Elsevier Ltd.

关键词： Stackelberg game Nonlinear bilevel programming nested genetic algorithm Emission control Joint decision making

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Optimizing Joint Production Planning, Pricing and Retailer Selection with Emission Control based on Stackelberg Game and nested genetic algorithm

Optimizing Joint Production Planning, Pricing and Retailer S...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Zhang, Linda L. Du, Gang Wu, Jun Ma, Yujie IESEG Sch Management Lille France LEM CNRS 9221 Lille France Tianjin Univ Coll Management & Econ Tianjin Peoples R China

ISBN: (纸本)9781728138046

We address the joint decision making of production planning, pricing, and retailer selection while considering emission control. In view of the interactive decision-making processes, we adopt the Stackelberg game theory and develop a 0-1 mixed nonlinear bi-level programming model to maximize the profits of a manufacturer and his retailers. Involving two submodels representing the decision-making processes of the manufacturer and retailers, respectively, the Stackelberg game model is very complex and impossible to be solved analytically. Therefore, we further develop a nested genetic algorithm (NGA) to solve the game model. Numerical examples demonstrate (i) the applicability of the Stackelberg game model in making joint decisions and (ii) the robustness of the NGA. Sensitivity analysis sheds light on several important managerial implications.

关键词： Joint decision making Stackelberg game nested genetic algorithm nonlinear bi-level program

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Joint optimisation of product family configuration and supply chain resilience

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Zeng, Ming Ye, Yangyang Luo, Xinggang Chen, Wenchong Zhang, Zhongliang Zeng, Ruochen Hangzhou Dianzi Univ Sch Management Hangzhou Peoples R China Hangzhou Dianzi Univ Expt Ctr Data Sci & Intelligent Decis Making Hangzhou Peoples R China Shanghai Univ SILC Business Sch Shanghai Peoples R China

A product family consists of multiple components, often supplied by various suppliers. It is crucial to consider the supply risk of these suppliers during product family configuration to ensure that orders can be replenished by alternative suppliers in the event of component disruptions. This study establishes a new optimisation model that concurrently addresses product family configuration and supply chain resilience for the first time. The model is further linearised, making it readily solvable using commercial optimisation software packages for smaller-scale problems. A nested genetic algorithm for solving the large-scale problems is also developed. The proposed method is demonstrated through a case study involving e-book products. The numerical results indicate that the joint optimisation method outperforms other methods, and the nested genetic algorithm achieves high-quality near-optimal solutions with good stability. A sensitivity analysis based on the e-book case assesses the impact of several parameters on profit, including supplier flexibility, unit loss cost, and disruption probability. The findings underscore the importance of supplier flexibility in supplier selection and suggest that companies should carefully evaluate unit loss costs across different module instances, as well as the regional risks associated with suppliers.

关键词： Product family supply chain resilience order allocation order reallocation nested genetic algorithm industry, innovation and infrastructure

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An Optimization Control Method of IEH Considering User Thermal Comfort

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ENERGIES 2024年第4期17卷 948页

作者： Zheng, Huankun Yu, Kaidi North China Elect Power Univ Sch Elect & Elect Engn Baoding 071000 Peoples R China

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system's operating costs and improve energy efficiency while satisfying user thermal comfort demand.

关键词： energy hub power router user thermal comfort level of customers nested genetic algorithm

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The Two Echelon Open Vehicle Routing Problem: Optimization of Crowdshipping Based Parcel Delivery

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KSCE JOURNAL OF CIVIL ENGINEERING 2022年第9期26卷 4073-4085页

作者： Wu, Xue Hu, Dawei Ma, Bingshan Jiang, Ruisen Changan Univ Sch Transportat Engn Xian 710064 Peoples R China Changan Univ Sch Automobile Xian 710064 Peoples R China

Crowdshipping is a revolutionary concept of the sharing economy. In this study, two carriers are used to perform the following expedition: the truck starts from the depot to complete part of the deliveries, shares part of the load with the crowdshipper at the relay point, and the private driver selected as the crowdshipper continues from that point onward. This study proposes the two-echelon open vehicle routing problem with crowdshipping (2EOVRP-CS) and formulates a mathematical model to determine the crowdshipper, parcel relay location, truck route, and crowdsource route. A tangible nested genetic algorithm (NGA) is proposed, and its efficiency is demonstrated by comparison with CPLEX and genetic algorithm (GA). A real case study is investigated in Xi'an city to test the applicability of the proposed model. The results show that using crowdshipping instead of truck delivery alone can save approximately 14% of the total cost and 26% of truck vehicle miles traveled (VMT). Moreover, several sensitivity analyses are performed. The results show that crowdshipping is sensitive to the detour limit and the time value of carriers. For the detour limit, after the acceptable detour distance increases by 8%, the total cost can be reduced by up to 5.94%.

关键词： Sharing economy Crowdshipping Two-echelon open vehicle routing problem Mixed-integer linear programming nested genetic algorithm

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A Bilevel Programming Approach for Optimizing Multi-Satellite Collaborative Mission Planning

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SENSORS 2024年第19期24卷 6242页

作者： Wang, Yi Liu, Desheng Space Engn Univ Natl Key Lab Space Target Awareness Beijing 101416 Peoples R China

With the burgeoning of remote sensing and space technology, multi-satellite collaborative mission planning, which is the key to achieving efficient Earth observation, has become increasingly intricate due to the expanding complexity and volume of observation missions. Addressing the multi-satellite collaborative mission planning problem, which is characterized by its two-stage decision-making process involving mission assignment and resource scheduling, this study investigates a comprehensive joint decision making that encompasses both mission assignment and resource scheduling and comprehensively optimizes the mission completion rate, the mission profit rate, and the satellite resource utilization rate. Considering the interaction of these decisions, we formulate the problem as a bilevel programming model from a game-theoretic perspective and propose a nested bilevel improved genetic algorithm (NBIGA) for its solution. Simulation experiments substantiate the applicability of the bilevel programming model in joint decision making for the stages of mission assignment and resource scheduling in multi-satellite collaborative mission planning, as well as the robustness of the NBIGA. A comparative analysis with the nested bilevel genetic algorithm (NBGA) confirms that the algorithm proposed in this study can achieve superior optimization outcomes and higher solving efficiency.

关键词： multi-satellite collaboration mission planning bilevel programming nested genetic algorithm

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Robust optimization of uncertain structures based on interval closeness coefficients and the 3D violation vectors of interval constraints

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2019年第1期60卷 17-33页

作者： Cheng, Jin Liu, Zhenyu Qian, Yangming Wu, Di Zhou, Zhendong Gao, Wei Zhang, Jia Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Harbin Inst Technol Minist Educ Key Lab Microsyst & Microstruct Mfg Harbin 150080 Heilongjiang Peoples R China Univ New South Wales Sch Civil & Environm Engn Sydney NSW 2052 Australia

In this paper, a novel direct interval robust optimization approach is proposed so that the robust optimal design vectors for structures with interval uncertainties can be achieved. A new concept of interval closeness coefficient is proposed to describe the relative positional relationship between the boundaries of two intervals. Subsequently, the explicit formulae for calculating the four interval closeness coefficients between the boundaries of an interval constraint mechanical performance index and those of its corresponding given interval constant are put forward. Consequently, the 3D violation vectors of different interval constraints can be calculated, and the feasibility of a design vector can be evaluated by its total 3D violation vector of all interval constraints. Finally, various design vectors are directly ranked according to the preferential guidelines considering the robustness of all the mechanical performance indices of uncertain structures, which is realized by integrating the Kriging technique and nested genetic algorithm. Unlike the traditional robust optimization of structures involving interval uncertainties, the proposed method can avoid the complicated model transformation process and ensure the robustness of all the mechanical performance indices of the optimal solution. Two examples are thoroughly investigated, the results of which demonstrate the applicability and advantages of the proposed approach.

关键词： Robust optimization Uncertain structure Interval closeness coefficient 3D violation vector of interval constraint Kriging technique nested genetic algorithm

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