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Multi-Objective Task Scheduling for Earth Observation InSAR Satellites via non- dominated sorting Student Psychology Based Optimization algorithm

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JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT 2025年 17卷

作者： Jia, Qingxian Lian, Weicheng Yu, Dan Sun, Qi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Peoples R China Nanjing Res Inst Elect Engn Nanjing Peoples R China

This paper investigates the task scheduling problem for the Earth observation Interferometric Synthetic Aperture Radar (InSAR) satellite system. The mission time window generation method is introduced, and the constraint satisfaction model for task scheduling in the InSAR satellite system is constructed. To address the mission allocation issue between the chief satellite and deputy satellites, a mission conflict detection and resolution mechanism is developed. Moreover, based on the single-objective student psychology-based optimization (SPBO) algorithm, a modified non-dominated sorting SPBO (NSSPBO) algorithm is proposed to tackle the multi-objective task scheduling problem for the InSAR satellite system. Numerical simulations are presented to demonstrate the effectiveness and superiority of the proposed NSSPBO algorithm.

关键词： InSAR satellite Task scheduling Student psychology based optimization algorithm non-dominated sorting algorithm

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Multi-Objective Mission Planning for Multi-Payload Satellite Constellation via non-dominated sorting Carnivorous Plant algorithm

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JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT 2024年 16卷

作者： Zhang, Yongkang Jia, Qinxian Wu, Yunhua Liao, He Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Jiangsu Peoples R China

This study investigates the issue of multi-objective mission planning for multi-payload satellite constellations via the nondominated sorting carnivorous plant algorithm (NSCPA). Observation time windows are generated, and a constraint satisfaction model is established based on multiple regional targets, satellite orbits, and characteristics of the synthetic aperture radar (SAR) payload and optical payload. A task conflict detection and resolution method is proposed to handle the task assignment among multiple satellites. Based on the existing single objective-based CPAs, a modified multi-objective NSCPA is first developed for multi-objective planning optimization using the non-dominated sorting algorithm. The effectiveness and superiority of the NSCPA are verified by a series of simulation experiments and comparisons with the traditional non-dominated sorting genetic algorithms-II (NSGA-II) and particle swarm optimization (PSO).

关键词： Satellite constellation Earth observatory satellite Mission planning non-dominated sorting algorithm Carnivorous plant algorithm

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A comprehensive study of various carbon-free vehicle propulsion systems utilizing ammonia-hydrogen synergy fuel

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ETRANSPORTATION 2024年 20卷

作者： Lei, Nuo Zhang, Hao Chen, Hu Wang, Zhi Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

Ammonia and hydrogen, as carbon-free clean energy, can be converted and applied in various scenarios. They can also be mixed to achieve synergistic efficiency. To promote the carbon-neutral development of heavy-duty vehicles, this paper studies an ammonia-hydrogen powertrain equipped with both a fuel cell and an engine (FCEAP). This powertrain efficiently allocates energy between multiple power sources and exploits the potential of ammonia-hydrogen synergy fuel. The modeling of FCEAP is based on experimental data obtained from engine bench tests, and the control strategy enables real-time control. Additionally, FCEAP undergoes multi-objective co-optimization using the non-dominated sorting algorithm-III (NSGA-III). By optimizing ammonia consumption, acceleration time, and manufacturing cost, Pareto solutions for the configuration and control strategy parameters are obtained. Furthermore, FCEAP is compared to ammonia-hydrogen powertrains equipped with either a fuel cell (FCAP) or an engine (EAP). The trade-off solutions indicate that FCEAP effectively balances energy consumption and manufacturing cost compared with FCAP and EAP. A comprehensive analysis of the energy flow distribution within various ammonia-hydrogen powertrains is conducted, revealing the operational processes and details of each component. The proposed ammonia-hydrogen powertrain represents an important technological pathway for achieving carbon neutrality in the future heavy-duty long-haul trucks industry.

关键词： Carbon-free vehicle propulsion system Ammonia-hydrogen synergy fuel Multi -objective optimization non-dominated sorting algorithm Energy flow distribution

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A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization

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ICT EXPRESS 2023年第1期9卷 92-99页

作者： Singh, Shalu Singh, Dinesh Manav Rachna Univ Dept Comp Sci & Technol Faridabad India Deenbandhu Chhotu Ram Univ Sci & technol Dept Comp Sci & Engn Murthal Sonepat India

Virtual Machine (VM) migration has become quite prevalent in cloud computing. Numerous VM migration-oriented techniques are required for efficient VM allocation but continue to struggle due to incorrect energy usage in the cloud model. Bio-inspired R&D-WOA (Re-initialization and Decomposition-Whale Optimization algorithm) is proposed by integrating WOA with NSGA-II algorithm to discover Pareto-optimal solutions. The proposed algorithm optimizes the allocation of the task to VM which diminishes the number of VM migrations, thereby lowering the migration cost and energy consumption. The efficiency of the Bio-inspired R&D-WOA is 67%, 7%, 1%, and 1.98% improved than the performance of existing WOA with regard to load, migration cost, energy utilized, and resource abundance respectively. At a similar level of load, it provided 24.80% efficiency in energy, 31.77% reduction in migration cost with maximal resource availability of 0.9917 compared to the ChicWhale algorithm.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： non-dominated sorting algorithm Energy Resource abundance VM migration Whale optimization

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non-traditional tolerance design techniques for low machining cost

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第5期17卷 2349-2359页

作者： Thilak, M. Jayaprakash, G. Paulraj, G. Bejaxhin, A. Bovas Herbert Nagaprasad, N. Buddhi, Dharam Gupta, Manish Jule, Leta Tesfaye Ramaswamy, Krishnaraj SRM TRP Engn Coll Dept Mech Engn Trichy Tamil Nadu India Saranathan Coll Engn Dept Mech Engn Trichy India SIMATS Dept Mech Engn Saveetha Sch Engn Chennai Tamil Nadu India ULTRA Coll Engn & Technol Dept Mech Engn Madurai 625104 Tamil Nadu India Uttaranchal Univ Div Res & Innovat Dehra Dun 248007 Uttarakhand India Lovely Profess Univ Div Res & Dev Phagwara India Dambi Dollo Univ Coll Nat & Computat Sci Dept Phys Dambi Dollo Ethiopia Dambi Dollo Univ Ctr Excellence Indigenous Knowledge Innovat Techn Dambi Dollo Ethiopia Dambi Dollo Univ Coll Engn & Technol Dept Mech Engn Dambi Dollo Ethiopia

In order to have a competitive edge, manufacturing companies have to develop superior quality products at minimum cost. Tolerance design is the most critical part of concurrent engineering in which optimal values of tolerances have to be determined for all components of an assembly, with due consideration towards the cost as well as quality. In this paper, tolerance design optimization of two products namely piston - cylinder & punch die assembly are considered. To solve the constraint-based optimization problems which are nonlinear and multi-objective in nature, novel techniques like Particle Swarm Optimization (PSO), and the non dominated sorting Genetic algorithm II (NSGA II) have been used. The results of the piston-cylinder assembly have been compared to those of complicated and evolutionary techniques like Simulated (SA) and Genetic algorithms (GA). In addition, their performances have been examined.

关键词： Tolerance allocation Optimization Particle swarm optimization non-dominated sorting algorithm

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Guided sampling-based evolutionary deep neural network for intelligent fault diagnosis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Sharma, Arun K. Verma, Nishchal K. Indian Inst Technol Dept Elect Engn Kanpur India

The selection of model architecture and hyperparameters has a significant impact on the diagnostic performance of most deep learning models. Because training and evaluating the various architectures of deep learning models is a time-consuming procedure, manual selection of model architecture becomes infeasible. Therefore, we have proposed a novel framework for evolutionary deep neural networks that uses a policy gradient to guide the evolution of the DNN architecture towards maximum diagnostic accuracy. We have formulated a policy gradient-based controller that generates an action to sample the new model architecture at every generation so that optimality is obtained quickly. The fitness of the best model obtained is used as a reward to update the policy parameters. Also, the best model obtained is transferred to the next generation for quick model evaluation in the NSGA-II evolutionary framework. Thus, the algorithm gets the benefits of fast non-dominated sorting as well as quick model evaluation. The effectiveness of the proposed framework has been validated on three datasets: the Air Compressor dataset, the Case Western Reserve University dataset, and the Paderborn University dataset.

关键词： Neural architecture search Intelligent fault diagnosis Deep neural network non-dominated sorting algorithm Policy gradient

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A non-dominated sorting genetic algorithm for a bi-objective pick-up and delivery problem

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ENGINEERING OPTIMIZATION 2012年第3期44卷 305-325页

作者： Velasco, N. Dejax, P. Gueret, C. Prins, C. Univ Los Andes Dept Ingn Ind Bogota Colombia Ecole Mines Nantes UMR 6597 IRCCyN F-44307 Nantes 3 France Univ Technol Troyes FRE 2848 ICD F-10010 Troyes France

Some companies must transport their personnel within facilities. This is especially the case for oil companies that use helicopters to transport engineers, technicians and assistant personnel from platform to platform. This operation has the potential to become expensive if the transportation routes are not correctly planned and provide a bad quality of service. Here this issue is modelled as a pick-up and delivery problem where a set of transportation requests should be scheduled in routes, minimizing the total transportation cost while the most urgent requests are satisfied by priority. To solve the problem, a method based on a non-dominated sorting Genetic algorithm (NSGA-II) is proposed. This algorithm is tested on both randomly generated and real instances provided by a petroleum company. The results show that the proposed algorithm improves the best-known solutions.

关键词： routing pick-up and delivery personnel transportation non-dominated sorting algorithm multi-objective genetic algorithm

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Location and allocation decisions for multi-echelon supply chain network - A multi-objective evolutionary approach

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第2期40卷 551-562页

作者： Shankar, B. Latha Basavarajappa, S. Chen, Jason C. H. Kadadevaramath, Rajeshwar S. Gonzaga Univ Grad Sch Business Spokane WA 99258 USA SIT Dept Ind Engg & Management Tumkur India UBDT Dept Mech Davangere India

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Four-echelon supply chain architecture Evolutionary approach non-dominated sorting algorithm MOHPSO Swarm intelligence

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