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Fuel-Optimal Multi-Impulse Orbit Transfer Using a hybrid optimization Method

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第4期21卷 1359-1368页

作者： Zhou, Hongyu Wang, Xiaogang Cui, Naigang Harbin Inst Technol Sch Astronaut Harbin 150001 Peoples R China

For the N-impulse transfer between two earth orbits, this paper introduces N - 1 intermediate orbits to describe the orbit transfer scheme. The eccentricity vector of the intermediate orbits and the true anomalies corresponding to the impulsive points are chosen as the optimization variables. Based on the patched conic theory, candidate solution can be analytically derived, constraints are removed from the optimization model, and the original problem is converted to a parameter optimization problem. The only difficulty lies in the initialization because the number of optimization variables increases linearly with N, which can be very large. This is settled by a hybrid optimization algorithm that comprises two searching methods. The problem is solved first by an improved particle swarm optimization method and, then, by an adaptive conjugate gradient method. The proposed method is adaptive to problems with any finite N and can calculate the optimal N in any transfer scenarios. The simulation validates the proposed method with some well-known cases and demonstrates its adaptation to N.

关键词： Multi impulse orbit transfer space transportation hybrid optimization algorithm

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Nature-Inspired Energy Enhancement Technique for Wireless Sensor Networks

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ENERGIES 2023年第20期16卷 7021页

作者： Hezekiah, James Deva Koresh Ramya, Karnam Chandrakumar Selvan, Mercy Paul Kumarasamy, Vishnu Murthy Sah, Dipak Kumar Devendran, Malathi Arumugam, Sivakumar Sabapathy Maheswar, Rajagopal KPR Inst Engn & Technol Dept Elect & Commun Engn Coimbatore 641407 Tamil Nadu India Sri Krishna Coll Engn & Technol Dept Elect & Elect Engn Coimbatore 641008 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India Sri Krishna Coll Technol Dept Elect & Elect Engn Coimbatore 641042 Tamil Nadu India GLA Univ Dept Comp Engn & Applicat Mathura 281406 Uttar Pradesh India Kongu Engn Coll Dept Elect & Commun Engn Erode 638060 Tamil Nadu India Dr NGP Inst Technol Dept Elect & Commun Engn Coimbatore 641048 Tamil Nadu India KPR Inst Engn & Technol Ctr IoT & AI CITI Dept Elect & Commun Engn Coimbatore 641407 Tamil Nadu India

Wireless Sensor Networks (WSN) play a major role in various applications, yet maintaining energy efficiency remains a critical challenge due to their limited energy availability. Network lifetime is one of the primary parameters for analyzing the performance of a WSN. This proposed work aims to improve the network lifetime of a WSN by enhancing its energy utilization through the Enhanced Monkey Search algorithm (E-MSA). The E-MSA provides an optimum solution for this issue by finding a better routing decision by analyzing the available energy on the nodes and the distance between the source and destination. Additionally, a Class Topper optimization (CTO) algorithm is also included in the work for determining an efficient node to be the cluster head and lead cluster head. In this technique, the data packets are collected by the lead cluster head from the other cluster heads for sending the information in a sequential manner to the base station for reducing data loss. A simulation model is implemented in the NS2 platform with 700 nodes in a 300 x 300 square meter area with 0.5 J of energy to each node for finding the efficiency of the proposed E-MSA with CTO algorithm over the traditional On-Demand Distance Vector (ODV) and Destination-Sequenced Distance Vector (DSDV) approaches. The experimental outcome indicates that the proposed work can reach a maximum lifetime of 1579 s which is comparatively better than the ODV and DSDV approaches by 212 and 358 s, respectively. Similarly, a packet delivery ratio of 79% is achieved with a throughput of 0.85 Mbps along with a delay of 0.48 s for the operation of all 700 nodes.

关键词： network enhancement lifetime improvement customized optimization energy conservation hybrid optimization algorithm

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A GV-drone arc routing approach for urban traffic patrol by coordinating a ground vehicle and multiple drones

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 77卷

作者： Xu Binjie Zhao Kexin Luo Qizhang Wu Guohua Pedrycz, Witold Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

With the continuous expansion of civilian field, unmanned aerial vehicle (UAV), also known as drone, has received much attention. Nevertheless, the flight range of a rotary wing drone is restricted due to limited battery endurance. To conquer this shortage, the coordination of ground vehicles (GVs) and drones to finish complex tasks has attracted much attention recently. Especially, the coordination of trucks and drones has been inves-tigated to realize the last-mile parcel delivery. By contrast, in this study, we propose to coordinate GVs and drones to monitor urban road networks more efficiently. The GV serves as a mobile carrier for launching, recovering, and charging the drones, while the drones are used to inspect some concerned sections of the road networks. To obtain high-quality routes for the GV and drones, a novel GV-drone arc routing problem (GVD-ARP) is specifically investigated. A mathematical model is constructed to describe the GVD-ARP and solved by the Gurobi optimizer. In addition, a hybrid optimization algorithm that combines the randomized variable neigh-borhood descent search, which is incorporated with the simulated annealing algorithm (RVND-SA), is developed to solve the model. Extensive experiments on instances with different scales are conducted and the results show the superiority of RVND-SA comparing with four other algorithms. Moreover, experiments on patrolling the real-world road networks are conducted, which further demonstrates the effectiveness of the model and RVND-SA.

关键词： Vehicle-drone coordination Arc routing problem hybrid optimization algorithm Variable neighborhood descent Traffic patrol

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A meta-optimized hybrid global and local algorithm for well placement optimization

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COMPUTERS & CHEMICAL ENGINEERING 2018年 117卷 209-220页

作者： Chen, Hongwei Feng, Qihong Zhang, Xianmin Wang, Sen Ma, Zhiyu Zhou, Wensheng Liu, Chen China Univ Petr East China Sch Petr Engn Qingdao 266580 Shandong Peoples R China CNOOC Res Inst Beijing 100027 Peoples R China

Well placement optimization is a complex and time-consuming task. An efficient and robust algorithm can improve the optimization efficiency. In this work, we propose a meta-optimized hybrid cat swarm mesh adaptive direct search (O-CSMADS) algorithm for well placement optimization. By coupling Cat Swarm optimization (CSO) algorithm, Mesh Adaptive Direct Search (MADS) algorithm, and Particle Swarm optimization (PSO) meta-optimization approach, O-CSMADS has global search ability and local search ability. We perform detailed comparisons of optimization performances between O-CSMADS, hybrid cat swarm mesh adaptive direct search (CSMADS) algorithm, CSO, and MADS in three different examples. Results show that O-CSMADS algorithm outperforms stand-alone CSO, MADS, and CSMADS. Besides, optimal controlling parameters are not same for different problems, which indicates that the optimization of algorithmic parameters is necessary. The proposed method also shows great potential for other petroleum engineering optimization problems, such as well type optimization and joint optimization of well placement and control. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Well placement optimization efficiency hybrid optimization algorithm Meta-optimization approach Cat swarm optimization algorithm Mesh Adaptive Direct Search

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Two-echelon multi-depot multi-period location-routing problem with pickup and delivery

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期182卷

作者： Wang, Yong Wang, Xiuwen Wei, Yuanhan Sun, Yaoyao Fan, Jianxin Wang, Haizhong Chongqing Jiaotong Univ Sch Econ & Management Chongqing 400074 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China Dalian Univ Technol Sch Econ & Management Dalian 116024 Liaoning Peoples R China Nanjing Univ Aeronaut & Astronaut Sch Econ & Management Nanjing 211106 Peoples R China Chongqing Jiaotong Univ Sch River & Ocean Engn Chongqing 400074 Peoples R China Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97330 USA

Efficient logistics network designs can contribute to operational cost savings and efficiency improvements. In this study, a two-echelon multi-depot multi-period location-routing problem with pickup and delivery (2EMDMPLRPPD) is proposed, seeking to construct a logistics network with high efficiency by optimizing periodic location decisions and routing schemes. On the basis of the changing time windows of customers and facilities, the proposed 2E-MDMPLRPPD explores their potential periodicity and divides the whole planning horizon into multiple periods for conducting location decisions and arranging customer services. Vehicle-sharing strategies, which encourage vehicles to be shared by facilities across multiple periods, have also been integrated into the 2EMDMPLRPPD optimization to improve resource utilization and sustainability. The 2E-MDMPLRPPD is formulated as a bi-objective mathematical model, with the minimization of the total operational cost and number of vehicles as the two objective functions. To solve the optimization model, this study develops a hybrid multiobjective particle swarm optimization (HMOPSO) algorithm and introduces a three-dimensional (3D) k-means clustering to assist the proposed hybrid algorithm. The 3D k-means is the key to providing initial feasible candidate location strategies and simplifying the logistics network, reducing the complexity and difficulty for the following further optimization by HMOPSO. The proposed HMOPSO, integrating the Clarke-Wright saving algorithm and variable neighborhood search (VNS) into the standard MOPSO, shows good performance in solving the proposed 2E-MDMPLRPPD optimization, which is demonstrated through an algorithm comparison with MOPSO, multi-objective VNS and non-dominated sorting genetic algorithm-II. Finally, the proposed model and algorithm are applied to a real-world case study of 2E-MDMPLRPPD in Chengdu, China. A two-period locationrouting strategy can achieve the best optimization results, and ba

关键词： Location-routing problem Vehicle sharing strategy 3D k -means clustering hybrid optimization algorithm Variable neighborhood search

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INVERSE AND DIRECT optimization SHAPE OF AIRFOIL USING hybrid algorithm BIG BANG-BIG CRUNCH AND PARTICLE SWARM optimization

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2019年第3期57卷 697-711页

作者： Masoumi, Heidar Jalili, Farhad Engn Dept Khoy Iran Azad Univ Ajabshir Iran

In this paper, Big Bang-Big Crunch and Particle Swarm optimization algorithms are combined and used for the first time to optimize airfoil geometry as a aerodynamic cross section. The optimization process is carried out both in reverse and direct directions. In the reverse approach, the object function is the difference between pressure coefficients of the optimized and target airfoils, which must be minimized. In the direct approach, three objective functions are introduced, the first of which is the drag to lift (D/L) ratio. It is minimized considering four different initial geometries, ultimately, all four geometries converge to the same final geometry. In other cases, maximizing lift the coefficient with the fixed drag co-efficient constraint and minimizing the drag coefficient while the lift coefficient is fixed are defined as purposes. The results show that by changing the design parameters of the initial airfoil geometry, the proposed hybrid optimization algorithm as a powerful method satisfies the needs with proper accuracy and finally reaches the desired geometry.

关键词： hybrid optimization algorithm airfoil inverse and direct optimization approaches Euler's equations

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An improved hybrid ant particle optimization (IHAPO) algorithm for reducing travel time in VANETs

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APPLIED SOFT COMPUTING 2018年 64卷 526-535页

作者： Jindal, Vinita Bedi, Punam Univ Delhi Dept Comp Sci Keshav Mahavidyalaya Delhi India Univ Delhi Dept Comp Sci Delhi India

With the increase in traffic volume day by day, cities are facing the problem of extreme congestion in most of the developing countries. The congestion on the road leads to increase in travel time and travel cost as well as having a significant impact on the health of people. This paper proposes a novel Improved hybrid Ant Particle optimization (IHAPO) algorithm for reducing the travel time for enabling smart transportation. The aim of the proposed algorithm is to select a best path in peak hours by avoiding the optimal path, if congested and resuming the optimal path when congestion eases. This algorithm is an improvement of the existing Modified Ant Colony optimization (MACO) algorithm. It combines both MACO and Particle Swarm optimization (PSO) algorithms using the global best exchange method. Initially, both algorithms work separately and produce their best solutions. Then a comparison has been made between both the solutions and a new global best solution found for the whole network. According to the best solution obtained, the position of both ants and particles are changed for the next iterations. MACO algorithm works under the assumption that all roads are in working condition, whereas the proposed IHAPO algorithm works under normal road conditions. Another difference between these algorithms is the pheromone update process that makes the new algorithm more effective. The proposed algorithm is tested on a map of North-West Delhi, India using Simulation of Urban MObility (SUMO) for traffic simulation. It was found that the travel time is reduced significantly by using the proposed IHAPO algorithm over the existing algorithms in consideration. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Ant colony optimization Nature inspired algorithms Particle swarm optimization hybrid optimization algorithm Vehicular adhoc networks

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A Novel hybrid algorithm for Solving Economic Load Dispatch in Power Systems

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷

作者： Fahim, Khairul Eahsun De Silva, Liyanage C. Andiappan, Viknesh Shezan, Sk. A. Yassin, Hayati Univ Brunei Darussalam Fac Integrated Technol Bandar Seri Begawan BE1410 Brunei Swinburne Univ Technol Fac Engn Comp & Sci Jalan Simpang Tiga Kuching Sarawak Malaysia Murdoch Univ Discipline Engn & Energy Perth Australia

Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented to explicitly evaluate ELD for test systems and real-world power plant systems matching the operational limitations. In addition, earlier algorithms could only offer estimates of the final cost of fuel based on the hyperparameter choices. This may prevent the global minimum values from being met. To find comprehensive solutions to the ELD problem in power systems, this paper suggests a new method called the hybrid Jaya optimization algorithm, which uses the merits of the Jaya and teaching-learning-based optimization (TLBO) algorithms. This enhancement is proposed to improve the population variety, the balance between local and global search, and the early convergence of the original Jaya optimization method. A metaheuristic optimization technique called TLBO simulates the teaching-learning process in a classroom to optimize problems. The TLBO algorithm uses an exploration phase in which possible solutions are generated at random to discover the best solution. The algorithm then uses the exploitation phase to refine the search space-based parameter adjustments to enhance the quality of the best solution identified. On the other hand, the Jaya algorithm is a metaheuristic optimization algorithm motivated by the idea of social behavior in nature. Candidate solutions are improved repeatedly through cooperation and competition using a population-based approach, and each solution adjusts its position based on the best and worst answers in the population. By combining the advantages of both algorithms, hybrid Jaya (Jaya-TLBO) outperforms each method alone and minimizes the cost of power generation, improving convergence solution quality. To test its efficacy, the hybrid Jaya-TLBO algorithm is tested on four different test c

关键词： artificial intelligence algorithm economic load dispatch (ELD) evolutionary algorithm hybrid optimization algorithm Jaya-TLBO algorithm power system optimization

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An Improved Scheme of Azimuthally Anisotropic Seismic Inversion for Fracture Prediction in Volcanic Gas Reservoirs

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Guo, Zhiqi Zhang, Xiaodong Liu, Cai Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130021 Peoples R China

Seismic prediction of natural fractures is essential for the characterization of unconventional reservoirs because fractures provide seepage paths for fluid migration and storage space for hydrocarbon accumulation. However, it remains challenging to robustly estimate anisotropic parameters for fracture characterization based on seismic inversion methods. We propose an improved inversion scheme for the robust and accurate estimation of anisotropic parameters using PP-wave azimuthal amplitude differences incorporated with a hybrid optimization algorithm. Modeling analysis indicates that by removing the effect of isotropic terms, azimuthal amplitude differences are more sensitive to anisotropic parameters than the traditional azimuthal reflection coefficient;this can avoid possible instabilities in anisotropic parameter estimates that may occur in conventional methods due to unbalanced weighting coefficients between isotropic and anisotropic terms. Meanwhile, the proposed hybrid algorithm takes advantage of the global optimization ability of the simulated annealing algorithm and the fast convergence characteristics of the particle swarm optimization algorithm. Synthetic data tests indicate that the hybrid algorithm achieves reliable and stable estimates of anisotropic parameters with higher computational accuracy and efficiency than traditional methods. The improved inversion method is applied to characterize fractures in volcanic gas reservoirs. Based on azimuthal amplitude differences obtained after estimating fracture orientation using the Fourier series method, anisotropic parameters are computed and converted to weakness properties, which is more meaningful in terms of the fracture properties. Our results indicate that the obtained fracture tangential weakness exhibits an apparent correspondence with well-log permeability, thus justifying the applicability of the proposed method for fracture prediction.

关键词： Azimuth Reservoirs Reflection coefficient Media Mathematical models Approximation algorithms Rocks Azimuthal amplitude difference fracture prediction hybrid optimization algorithm seismic anisotropy volcanic gas reservoir

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An Optimized Modelling and Simulation on Task Scheduling for Multi-Processor System using hybridized ACO-CVOA

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2022年第5期38卷 895-907页

作者： Priya, Annu Sahana, Sudip Kumar Birla Inst Technol Mesra Dept Comp Sci Engn Ranchi 835215 India

Task allocation on the multi-processor system distributes the task according to capac-ity of each processor that optimally selects the best. The optimal selection of processor leads to increase performance and this also impact the makespan. In task scheduling, most of the research work focused on the objective of managing the power consumption and time complexity due to improper selection of processors for the given task items. This paper mainly focusses on the modelling of the optimal task allocation using a novel hy-bridization method of Ant Colony optimization (ACO) with Corona Virus optimization algorithm (CVOA). There are several other methods that estimate the weight value of processors and find the best match to the task by using the traditional distance estimation method or by using standard rule-based validation. The proposed algorithm searches the best selection of machines for the corresponding parameters and weight value iteratively and finally recognizes the capacity of it. The performance of proposed method is evaluated on the parameters of elapsed time, throughput and compared with the state-of-art methods.

关键词： task allocation hybrid optimization algorithm ant colony optimization (ACO) corona virus optimization algorithm (CVOA) multiprocessor

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