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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Dhanalakshmi, S. Kannan, S. Baskar, S. Mahadevan, K. Velammal Coll Engn & Technol Madurai Tamil Nadu India Ramco Inst Technol Rajapalayam India Thiagarajar Coll Engn Madurai Tamil Nadu India PSNA Coll Engn & Technol Dindugal India

ISBN: (纸本)9783319202945;9783319202938

This paper presents a Modified non-dominated sorting genetic algorithm-ii (MNSGA-ii) solution to Multi-objective Generation Scheduling (MOGS) problem. The MOGS problem involves the decisions with regards to the unit start-up, shut down times and the assignment of the load demands to the committed generating units, considering conflicting objectives such as minimization of system operational cost and minimization of emission release. Through an intelligent encoding scheme, hard constraints such as minimum up/down time constraints are automatically satisfied. For maintaining good diversity in the performance of NSGA-ii, the concepts of Dynamic Crowding Distance (DCD) is implemented in NSGA-ii algorithm and given the name as MNSGA-ii. In order to prove the capability of the proposed approach 10 units, 24-hour test system is considered. The performance of the MNSGA-ii are compared with NSGA-ii and validated with reference Pareto front generated by conventional weighted sum method using Real Coded genetic algorithm (RGA). Numerical results demonstrate the ability of the proposed approach, to generate well distributed pareto front solutions for MOGS problem.

关键词： Dynamic crowding distance Emission Generation scheduling Multi-objective optimization non-dominated sorting genetic algorithm-ii Real coded genetic algorithm unit commitment

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Multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors

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APPLIED INTELLIGENCE 2025年第1期55卷 1-14页

作者： Zhang, Quanyong Li, Hui Shen, Shengnan Cao, Wan Jiang, Jing Tang, Wen Hu, Yuanshun Wuhan Univ Inst Technol Sci Wuhan 430072 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China Wuhan Univ Hubei Prov Engn Res Ctr Integrated Circuit Packagi Wuhan 430072 Peoples R China Wuhan Univ Hubei Key Lab Elect Mfg & Packaging Integrat Wuhan 430072 Peoples R China Wuhan Fine MEMS Inc Wuhan 430075 Peoples R China Wuhan Huagong Cyber Data Syst Co Ltd Wuhan 430074 Peoples R China

Intelligent manufacturing can provide powerful support for the digital transformation of manufacturing industry. Micro-electro-mechanical system (MEMS) sensors have been widely used in the automotive industry because of their small size, low cost, and high reliability. Aiming at the problems of low flexibility, poor adaptability, and high manufacturing cost in the mixed-flow intelligent production line of multi-variety automotive MEMS pressure sensors in this study, a multi-objective optimization model is established with takt time and balance rate as optimization objectives. The non-dominated sorting genetic algorithm-ii (NSGA-ii) is used to obtain the multi-objective optimization of the mixed-flow intelligent production line with the elite strategy, crowding degree, and crowded comparison operator. The accuracy of the NSGA-ii is validated by comparing it with that of the ant colony optimization (ACO) algorithm, simulated annealing (SA) algorithm, and particle swarm optimization (PSO) algorithm. The NSGA-ii achieves higher optimization accuracies for takt time and balance rate compared to ACO, SA, and PSO algorithms. Specifically, the NSGA-ii achieves optimization accuracies of 2.73%, 2.44%, and 8.99% for the takt time, slightly surpassing those of the ACO, SA, and PSO algorithms respectively. Similarly, for the balance rate, the NSGA-ii achieves optimization accuracies of 2.17%, 1.89%, and 2.48%, slightly higher than those of the ACO, SA, and PSO algorithms respectively. The takt time is optimized by NSGA-ii to less than 10 s/piece, while the balance rate is optimized to over 90%. The multi-objective optimization of the mixed-flow intelligent production line for automotive MEMS pressure sensors is practical and instructive for improving production line efficiency.

关键词： Multi-objective optimization Takt time Balance rate non-dominated sorting genetic algorithm-ii

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Multi-objective optimization towards cooling performance of teardrop section Kagome trusses array jet impingement composite cooling structure with cross-flow

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APPLIED THERMAL ENGINEERING 2025年 258卷

作者： Ran, Hongxin Xu, Liang Xi, Lei Gao, Jianmin Li, Yunlong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Teardrop Section Kagome trusses array can be a spoiler for a new generation of advanced combustion chambers. In this study, multi-objective optimizations are applied to optimize a jet impingement composite cooling structure with cross-flow. Four structural parameters with five levels and three performance parameters are calculated in design of experiment and fitted by response surface methodology. The database is expanded by nondominated sorting genetic algorithm-ii, and then technique for order preference by similarity to an ideal solution is used to calculate the optimal structures under different operating conditions by different weights of the performance parameter. The conclusions are as follows: The minimal Equality of variances and fitting accuracy are 35.4371 and 93.6%. Diameter of the cross-section has the largest positive main effect on average Nusselt number and pressure loss coefficient, while angle between the rod and the horizontal plane has the largest positive effect on temperature uniformity coefficient. The interaction of trailing edge angle with angle between the front rod and the back rod in the horizontal plane is large. Case15 is chosen, where the weight ratio is 1:5:5, improvement of the corresponding optimized performance parameters of the simulation results with respect to the Origin of three performance parameters are 26.72%, -2.93%, 91.59% respectively. It provides a feasible reference for future advanced combustion chambers and accelerates their development process.

关键词： Trusses array Multi-objective optimization Response surface methodology non-dominated sorting genetic algorithm-ii TOPSIS

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Modelica based hybrid-dimensional dynamic modeling, multi-objective optimization and thermodynamic analysis of cross-flow SOFC system

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RENEWABLE ENERGY 2025年 241卷

作者： Xia, Lei Khosravi, Ali Han, Minfang Sun, Li Southeast Univ Natl Engn Res Ctr Power Generat Control & Safety Sch Energy & Environm Nanjing 210096 Peoples R China Univ Southern Denmark Dept Mech & Elect Engn SDU Mechatron DK-6400 Sonderborg Denmark Univ Southern Denmark Ctr Ind Mech Dept Mech & Elect Engn DK-6400 Sonderborg Denmark Tsinghua Univ Dept Energy & Power Engn State Key Lab Power Syst Beijing 100084 Peoples R China

The cross-flow configuration has obvious advantages for the fabrication of solid oxide fuel cell (SOFC) stacks, but results in a complex distribution of variables within the cell. This study introduces a cross-flow SOFC system designed to enhance unreacted hydrogen recovery and air recycling. The optimization framework formed by combining system simulation, Artificial Neural Network (ANN) surrogate model and non-dominated sorting genetic algorithm-ii (NSGA-ii) is applied to improve the system's comprehensive performance. Then, the steadystate performance and the dynamic responses of the system are analyzed. The results show that the ANN model has high prediction precision with coefficients of determination greater than 0.9999, and the optimization framework can implement a fast-global optimization of the system. The SOFC power and efficiency of the optimized system are 80.2024 kW and 61.97 % respectively, and the fuel utilization of the system is 99.92 %. The maximum temperature gradient of SOFC is less than 10 K/cm and the standard deviation of the temperature distribution is 18.8664 K. The SOFC temperature of the optimal system increases and then decreases along the hydrogen flow direction. The step change of current and air flow causes different dynamic responses of the system, especially significant differences in SOFC voltage, and system efficiency.

关键词： SOFC Cross-flow Artificial neural network non-dominated sorting genetic algorithm-ii Dynamic response

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Security-aware resource allocation in fog computing using a meta-heuristic algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-18页

作者： Mohammadi, Mina Bahranipour, Fatemeh Mood, Sepehr Ebrahimi Farshi, Mohammad Yazd Univ Dept Comp Sci Daneshgah Blvd Yazd *** Iran

In recent years, the number of smart devices and wireless data transmissions has increased worldwide. These emerging applications and services require not only extensive computing capabilities and high battery power, but also elevated data transmissions as well. nonetheless, the computing capacity of this equipment is constrained, resulting in significant consequences on the performance and operating costs of services in 5th-generation wireless networks. Recent advantages of Fog computing have increased the use of this model to fulfill above requirements in the IoT context. A new Fog computing network model has been proposed in order to address these issues by providing cloud computing services at the network's edge. In Fog computing, mobile devices are not required to offload all their tasks to remote and central servers. However, since other users are exposed to offloaded tasks, they are vulnerable to malicious attacks and eavesdropping. In this paper, we investigate security-aware resource allocation in device-to-device based fog computing systems. In order to enhance task offloading, a novel multi-objective function is proposed to optimize delay and energy savings compared to local computing, as well as security breach costs. Several multi-objective meta-heuristic algorithms, such as non-dominated sorting genetic algorithm ii (NSGA-ii), have been proposed over the past decade. Optimizing objectives, such as energy consumption and delay was the goal of using these algorithms. Subsequently, the improved NSGA-ii algorithm is employed to solve the problem. In this algorithm, the Sigma Scaling, a technique used to adjust fitness values to maintain diversity in the population, is utilized to control selection pressure. By incorporating Sigma Scaling, the exploration and exploitation capabilities of the algorithm are effectively managed, enhancing its ability to escape local optima and prevent premature convergence. The performance of our modified NSGA-ii is superior t

关键词： non-dominated sorting genetic algorithm-ii Security aware Device-to-device communication Meta-Heuristic algorithm

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Catalyzing Sustainable Energy Transition in Oman: A Peer-to-Peer Energy Trading Model With Integrated GHG Emission Mitigation Strategies

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IEEE ACCESS 2025年 13卷 27098-27111页

作者： Al-Abri, Tariq Onen, Ahmet Khan, Muhammad Waseem Al-Abri, Rashid Alrawahi, Ahmed Salim Al-Saadi, Saleh Sultan Qaboos Univ Coll Engn Dept Elect & Comp Engn Muscat 123 Oman Univ Doha Sci & Technol Coll Engn & Technol Dept Elect Power & Renewable Energy Doha Qatar Sultan Qaboos Univ Sustainable Energy Res Ctr SERC Muscat 123 Oman Univ Nizwa Coll Engn & Architecture Nizwa 616 Oman Univ Technol & Appl Sci Coll Comp & Informat Sci Dept Informat Technol Ibri 516 Oman Sultan Qaboos Univ Coll Engn Dept Civil & Architectural Engn Muscat 123 Oman

Exploring the transformative potential of a peer-to-peer (P2P) energy trading model, this study examines how integrating greenhouse gas (GHG) emission mitigation strategies can facilitate a sustainable energy transition in Oman. This paper proposes a new P2P energy trading framework incorporating GHG credits to enable direct energy trading between prosumers and consumers in a decentralized network without intermediaries. The proposed framework prioritizes energy trading between prosumers based on their GHG credits. It employs the non-dominated sorting genetic algorithm-ii (NSGA-ii) to determine the energy and GHG selling prices by optimizing three objective functions: maximizing prosumers' profits, minimizing consumers' costs, and reducing the penalty associated with prosumers' power loss factor (PLF) due to their use of the grid network. The NSGA-ii algorithm constraints are investment-driven, with energy cost limits based on PV investment payback periods and GHG allowance cost limits derived from international GHG trading data. This approach ensures the social welfare of all participants while accounting for system losses incurred by transacting peers. The proposed framework was tested in different case studies on the IEEE 14-bus system with three prosumers and eight consumers by using data in Oman grid. The proposed framework concept offers a practical approach to energy trading that considers various operating conditions and potential losses, ensuring more efficient and resilient transactions within diverse market environments.

关键词： Peer-to-peer computing Renewable energy sources Costs Carbon dioxide Power system stability Fluctuations Wind energy generation Stability analysis Pricing Greenhouse gases Sustainable energy peer-to-peer greenhouse gas power loss energy trading non-dominated sorting genetic algorithm-ii

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Multi-objective optimization of syngas production from bio-glycerol: A bi-reforming approach using NSGA-ii

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第3期13卷

作者： Zol, Santosh Chandodkar, Hrushikesh Ahmed, Mukhtar Haider, Mohd Belal Kumar, K. D. P. Lakshmee Naidu, B. Neelam Kumar, Rakesh Viswanadham, Nagabhatla Inst Chem Technol Dept Chem Engn Jalna Maharashtra India Indian Inst Technol Dept Chem Engn Mumbai 400076 India Indian Inst Petr Light Stock Proc Div Dehra Dun India Rajiv Gandhi Inst Petr Technol Chem Engn Dept Amethi India

This study investigates the multi-objective optimization (MOO) of syngas production from bio-glycerol using the non-dominated sorting genetic algorithm-ii (NSGA-ii) for constrained optimization in the bi-reforming process. Thermodynamic modelling of the system was conducted in Aspen Plus V12, applying the Gibbs free energy minimization method and accounting for all by-products. Key decision variables, including the water-to-glycerol ratio, CO2-to-glycerol ratio, and reforming temperature, were examined for their impact on process performance. Results demonstrated that bi-reforming glycerol is more advantageous for syngas production compared to dry reforming, as it operates at lower temperatures, reducing coke formation. A glycerol bi-reforming process was simulated and optimized using MOO to maximize syngas production and CO2 conversion while minimizing exergy loss and CO2 emissions. The optimization revealed trade-offs among the objectives, with the Pareto front showcasing optimal solutions. Specifically, a higher water-to-glycerol ratio favoured a higher H-2-to-CO ratio but led to decreased CO2 conversion and increased energy consumption and CO2 emissions. The net flow method (NFM) is used to select the best optimal solutions from the Pareto front solutions based on the weight of the objective functions. Despite these trade-offs, bi-reforming of bio-glycerol was shown to be a viable method for producing syngas with a high H-2/CO ratio and low CO2 emissions. The best optimal solutions obtained for the bi-reforming of glycerol show an SGR of similar to 0.44 and CGR of 1.89, with the reforming temperature of 1202 K giving a CO2 conversion of 42.55 % with exergy efficiency of 78.3 % and syngas production of 6.29 per mole of glycerol having H-2/CO ratio of 3.16.

关键词： Glycerol bi-reforming Glycerol value addition Thermodynamic analysis Multi-objective optimization Gibbs free energy minimization non-dominated sorting genetic algorithm-ii

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Optimization of Sustainable Vehicle Routing Problem Taking into Account Social Utility and Employing a Strategy with Multiple Objectives

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1631-1658页

作者： Gouraji, R. Ebrahimi Soleimani, H. Najafi, B. Afshar Islamic Azad Univ Dept Ind Engn Qazvin Branch Qazvin Iran

In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle *** primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social *** achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-ii) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route *** results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustain

关键词： Exchange Locations Vehicle Routing Problem algorithms non-dominated sorting genetic algorithm-ii Multi-objective Particle Swarm Optimization Metaheuristic Time Constraint

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An Optimization of multi-level multi-objective cloud production systems with meta-heuristic algorithms

Decision Analytics Journal

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Decision Analytics Journal 2025年 14卷

作者： Razdar, Mohammadreza Adibi, Mohammad Amin Haleh, Hassan Department of Industrial Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Industrial Engineering Golpayegan Isfahan University of Technology Golpayegan Iran

The inability of small companies to compete with large production systems has led to the sharing resources in cloud production systems among smaller companies to compensate for their production deficiencies. This study proposes a multi-objective, multi-level cloud production system by minimizing the maximum completion time of activities, the costs of the entire cloud production system, and the maximum risk of information disclosure. We use a support vector machine (SVM) to train the input data, including activities, micro activities, and services of production units. The correlation between the trained and predicted data from the machine learning model equals 0.9977, indicating this method's high accuracy and efficiency. The Lp-Metric, multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm-ii (NSGA-ii) algorithms were used to solve the problem using trained input data. The Lp-Metric results show that reducing the completion time of all activities and the maximum risk of information disclosure increases the costs of the entire cloud production system. We also examine the efficiency of the solution methods and demonstrate that MOGWO is more efficient in solving the cloud production system problem. © 2024 The Author(s)

关键词： Cloud production system Information disclosure risk Machine learning Multi-objective grey wolf optimizer non-dominated sorting genetic algorithm-ii Trustworthiness index

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Prediction of fatigue life of laminated composites by integrating artificial neural network model and non-dominated sorting genetic algorithm

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INTERNATIONAL JOURNAL OF FATIGUE 2024年 188卷

作者： Mirzaei, A. H. Haghi, P. Shokrieh, M. M. Iran Univ Sci & Technol Ctr Excellence Expt Solid Mech & Dynam Sch Mech Engn Composites Res Lab Tehran *** Iran

The present study introduces an artificial neural network (ANN) model coupled with the non-dominated sorting genetic algorithm (NSGA-ii) to establish an advanced model for fatigue life prediction of laminated composites. The developed model was trained on a comprehensive collection of dependent experimental data of carbon/ epoxy composites. This training encompassed 14 distinct cases, covering various stress concentration geometries, loading levels, and stacking sequences to ensure the model's robustness and generality. A data augmentation technique was employed to generate similar data to enrich the datasets. The best-obtained accuracy from the model, as measured by the coefficient of determination (R2 score), was 88% and 90% for the test and validation datasets, respectively. While using the data augmentation method, these values improved to 97% and 98%, respectively. The results from the model were compared to those of more conventional regression machine learning algorithms, such as decision trees and gradient boosting. To emphasize the superior predictive accuracy of the developed model, it is essential to note that the best R2 score obtained for the test and validation datasets was 68% using conventional models.

关键词： Laminated composites Fatigue life prediction Artificial neural network non-dominated sorting genetic algorithm-ii

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