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检索条件"主题词=Optimization Algorithm"

共 3046 条记录，以下是211-220 订阅

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AOAB - Automated optimization algorithm Benchmarking 10

AOAB - Automated Optimization Algorithm Benchmarking

引用

12th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Weise, Thomas Niu, Li Tang, Ke Univ Sci & Technol China Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781450300735

In this paper we present AOAB, the Automated optimization algorithm Benchmarking system. AOAB can be used to automatically conduct experiments with numerical optimization algorithms by applying them to different benchmarks with different parameter settings. Based on the results, AOAB can automatically perform comparisons between different algorithms and settings. It can aid the researcher to identify trends for good parameter settings and to find which algorithms are suitable for which type of problem. We introduce the system structure of AOAB (the server and the graphical client interface), define the way in which optimizers and benchmark functions can be implemented for the use in AOAB, and conduct an illustrative example experiment with our system: a comparison between Random Search and two Hill Climbers.

关键词： AOAB Automated Benchmarking optimization algorithm Distribution Single-Objective optimization Numerical optimization

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Research and Application of optimization algorithm Based on Two-stage in Distribution Routing Planning

Research and Application of Optimization Algorithm Based on ...

引用

IEEE International Conference on Automation and Logistics

作者： He, Minai Zhu, Menggao Univ Shandong Jiaotong Dept Transporationt & Logist Engn Jinan Shandong Peoples R China Wuhan Univ Technol Transportat Sch Wuhan Peoples R China

ISBN: (纸本)9781424425020

In this paper, author comes up with a new optimization algorithm about distribution routing planning. This method is used to make the best distribution route according to vehicle's load capacity, distance from distribution centre and various client demand as well as cargo priority etc. The result could be displayed in electronic map. Proved by instances, this method can help managers to reduce the cost of distribution and make decisions for transportation control.

关键词： Distribution VRP optimization algorithm Logistics Informationization

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A Novel optimization algorithm: The Forest algorithm 5

A Novel Optimization Algorithm: The Forest Algorithm

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5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Wu, Qi Chengdu Med Coll Chengdu 610500 Sichuan Peoples R China

ISBN: (纸本)9781479942626

A new optimization algorithm, namely the Forest algorithm (FA), is introduced for the first time. This algorithm simulates trees' growth, reproduction and death in a forest to perform optimization. In the algorithm, trees and branches represent a collection of trial solutions and parameters needed to be optimized respectively, and three mechanisms, i.e. growth, proliferation and death, are employed for improving trees' vitality, which is a factor defined to evaluate the fitness of trial solutions. This algorithm in general execute a global optimization by operating on a group of trial solutions in parallel, but its growth mechanism, which adopts a parameter sweeping method, is a local optimization, so it combines the ability to find global optima of the global optimization and the fast convergence of the local optimization. Several numerical experiments are conducted, in which the performance of the FA in terms of the global optimization capability, accuracy and efficiency is evaluated and compared to that of some widely-used global optimization algorithms such as the Genetic algorithm (GA) and the Particle Swarm optimization (PSO). Results shown the FA is able to perform global optimization effectively and with high accuracy.

关键词： optimization algorithm Forest algorithm global optimization

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Stem Cells optimization algorithm

引用

7th International Conference on Intelligent Computing (ICIC)

作者： Taherdangkoo, Mohammad Yazdi, Mehran Bagheri, Mohammad Hadi Shiraz Univ Fac Elect & Comp Engn Dept Commun & Elect Shiraz Iran Shiraz Univ Med Radiol Med Imaging Res Ctr Shiraz Iran

ISBN: (纸本)9783642245527;9783642245534

optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums, having low speed of convergence, and also having high order of complexity for implementation. In this paper, we introduce a new optimization algorithm, we called it Stem Cells algorithm (SCA), which is based on behavior of stem cells in reproducing themselves. SCA has high speed of convergence, low level of complexity with easy implementation process. It also avoid the local minimums in an intelligent manner. The comparative results on a series of benchmark functions using the proposed algorithm related to other well-known optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and artificial bee colony (ABC) algorithm demonstrate the superior performance of the new optimization algorithm.

关键词： optimization algorithm Genetic algorithm (GA) Particle Swarm optimization (PSO) Ant Colony optimization (ACO) Artificial Bee Colony (ABC) algorithm Stem Cells algorithm (SCA)

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PCNN Document Segmentation Method Based on Bacterial Foraging optimization algorithm

PCNN Document Segmentation Method Based on Bacterial Foragin...

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6th International Conference on Digital Image Processing (ICDIP)

作者： Liao, Yanping Zhang, Peng Guo, Qiang Wan, Jian Harbin Engn Univ Sch Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781628411867

Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.

关键词： bacterial foraging optimization algorithm PCNN image segmentation

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DeP-D: A Decentralized Primal-Dual optimization algorithm for Industrial Wireless Sensor Networks 15

DeP-D: A Decentralized Primal-Dual Optimization Algorithm fo...

引用

15th IEEE International Workshop on Factory Communication Systems (WFCS)

作者： Farag, Hossam Gidlund, Mikael Osterberg, Patrik Mid Sweden Univ Dept Informat Syst & Technol Sundsvall Sweden

ISBN: (纸本)9781728112688

Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low delay is a key feature of delay-sensitive applications as the data is typically valid for a short interval of time. If data arrives too late it is of limited use which may lead to performance drops or even system outages which can create significant economical losses. In this paper, we propose a decentralized optimization algorithm to minimize the End-to-End (E2E) delay of multi-hop IWSNs. Firstly, we formulate the optimization problem by considering the objective function as the network delay where the constraint is the stability criteria based on the total arrival rate and the total service rate. The objective function is proved to be strictly convex for the entire network, then a Decentralized Primal-Dual (DeP-D) algorithm is proposed based on the sub-gradient method to solve the formulated optimization problem. The performance of the proposed DeP-D is evaluated through simulations and compared with WirelessHART network and the results show that the proposed DeP-D can achieve at least 40% reduction in the average E2E delay.

关键词： Industrial wireless sensor networks end-to-end delay optimization algorithm

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Multi-Objective Whale optimization algorithm for Optimal Allocation of Distributed Generation and Capacitor Bank

Multi-Objective Whale Optimization Algorithm for Optimal All...

引用

International Conference on Innovative Trends in Computer Engineering (ITCE)

作者： Saleh, Ayat Ali Mohamed, Al-Attar Ali Hemeida, A. M. Ibrahim, Abdalla Ahmed Aswan Univ Fac Energy Engn Aswan Egypt Aswan Univ Fac Engn Aswan Egypt

ISBN: (纸本)9781538652619

In this paper, proposed optimization technique called whale optimization algorithm (WOA) is presents to find the optimum allocation of distributed generation (DG) and capacitor in radial distribution systems during reduction of single and multi-objective function namely, (network power losses, voltage deviation, and total operating cost). The multi objective function is formed by the use of weighted sum method. In this paper, multiple-DG units have been analyzed under two load power factors (i. e., unity and optimal) with and without capacitor (C). WOA technique has been applied to a 33-bus radial distribution system. The performance of the WOA technique is compared with other evolutionary optimization methods under different operating conditions of the system. The impact of integrating the proper size of DG and C at the suitable placement based on proposed algorithm are shown in the simulation results.

关键词： optimization algorithm Radial Distribution system Distributed generation Capacitor banks Power loss reduction voltage deviation improvement total operating cost reduction

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A Mixed Integer Efficient Global optimization algorithm with Multiple Infill Strategy - Applied to a Wing Topology optimization Problem

A Mixed Integer Efficient Global Optimization Algorithm with...

引用

AIAA Science and Technology (SciTech) Forum and Exposition

作者： Roy, Satadru Crossley, William A. Stanford, Bret K. Moore, Kenneth T. Gray, Justin S. Purdue Univ Aeronaut & Astronaut 701 W Stadium Ave W Lafayette IN 47907 USA NASA Langley Res Ctr Aeroelastic Branch 218 Dodd Blvd Hampton VA 23681 USA DB Consulting Grp Inc Cleveland OH 44135 USA NASA Glenn Res Ctr Cleveland OH 44135 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

With the advancement in high performance computing and numerical optimization techniques, engineering design optimization problems are becoming more complex, larger scale, higher fidelity, and computationally more demanding, requiring longer run times than ever before. There exists methodologies and techniques that can address some of these challenges but very few can address all, and most are limited in the extent that these concerns can be addressed. With the goal of addressing such challenging engineering problems, we developed a new optimization algorithm, named AMIEGO, that combines concepts from surrogate-based optimization approaches, gradient-based numerical methods, Partial Least Squares, evolutionary algorithms, and Branch-and-Bound, providing newer capabilities that were not previously perceived. The effort here builds upon this previously developed optimization algorithm to include multiple infill sampling capability that combines the concept of generalized expected improvement function, unsupervised learning, and multi-objective evolutionary technique. To demonstrate, AMIEGO with the multiple infill capability (called AMIEGO-MIMOS) solves a series of increasingly difficult engineering design optimization problems. The results reveal the performance of the new approach is problem dependent. When applied to a ten-bar truss problem, the newly proposed multiple infill strategy consistently leads to a better design solutions when compared to the existing CPTV method (implemented with the context of the AMIEGO algorithm). On the other hand, when applied to a mixed-integer high fidelity wing topology optimization problem - MIMOS, despite showing a steeper convergence at the start, eventually leads to an inferior solution as compared to CPTV approach. These results also reveal that a small number of starting points, in general, are sufficient to lead to a good overall solution.

关键词： optimization algorithm Numerical optimization Trusses Multi Objective Evolutionary algorithms High Performance Computing Surrogate Model Wingbox Kernel Functions Parallel Computing Aspect Ratio

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A Metaheuristic optimization algorithm for Flight Assignment to the North Atlantic Organized Track System

A Metaheuristic Optimization Algorithm for Flight Assignment...

引用

AIAA Aviation Forum and Exposition

作者： Izadi, Arman Trani, Antonio A. Virginia Polytech Inst & State Univ Dept Civil & Environm Engn 200 Patton Hall Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Dept Civil & Environm Engn 200 Patton Hall Blacksburg VA 24061 USA

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

The North Atlantic region is the busiest oceanic airspace controlled by the United States. To better serve the two traffic flows between Europe and North America, two Organized Track Systems (OTS) are created on a daily basis to accommodate oceanic flights. This paper studies the potential benefits of a novel method for assigning flights to the OTS in the North Atlantic airspace. A mathematical model for the group assignment procedure is provided in this study, and a metaheuristic optimization algorithm is proposed for the solution methodology. The proposed algorithm is validated with a small size problem verifying it can find the global optimal in reasonable computational time. The validated optimization algorithm is implemented inside a fast-time flight simulation tool, called Global Oceanic Model. This study used the real-scale flight traffic related to three consecutive days in June 2016. The simulation results show the operational benefits of the proposed assignment procedure in terms of efficiency, level of service, and airline equity.

关键词： optimization algorithm North Atlantic Airspace Mathematical Models Flight Simulation Flight Level Controller Pilot Data Link Communications Mach Numbers Fuel Consumption Annealing Process Air Navigation Service Provider

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Multi-Agent Chaos Particle Swarm optimization algorithm of Thrust Allocation for dynamic positioning vessels 34

Multi-Agent Chaos Particle Swarm Optimization Algorithm of T...

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第三十四届中国控制会议

作者： XU Guang-chi ZHAO Da-wei ZHANG Kai-wei GE Jing-hua College of Mechanics Engineering Heilongjiang Agricultural Engineering Vocational College College of Automation Harbin Engineering University

ISBN: (纸本)9781467374439

As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated as nonlinear optimization problems. A chaos Particle Swarm optimization(PSO) algorithm combined with multi-agent scheme is proposed for the thrust allocation in this paper. The algorithm which uses multi-agent topological structure has three functions that keeps the diversity of the particle swarm population, improving particle swarm global search ability, and enhancing information diversity. Relying on chaotic local search to get rid of local optima, it can also improve the convergence precision. The numerical simulations are conducted to demonstrate the effectiveness of the proposed methods, and the results are compared with PSO algorithm.

关键词： Dynamic positioning optimization algorithm thrust allocation Multi-Agent CPSO

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