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global optimization algorithm for Cloud Service Composition

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第10期E104D卷 1580-1591页

作者： Yang, Hongwei Xue, Fucheng Liu, Dan Li, Li Feng, Jiahui Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun Peoples R China

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

关键词： web service composition beetle antennae search algorithm particle swarm optimization algorithm global optimization algorithm

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global optimization algorithm and Sub-Optimal algorithms for Power Allocation in Two-Way Relay Assisted Cognitive Radio Networks

Global Optimization Algorithm and Sub-Optimal Algorithms for...

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5th International Conference on Signal Processing and Communication Systems (ICSPCS)

作者： Pareek, U. Lee, Daniel C. Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781457711800

In this paper, we consider a two-way cognitive relay network comprising two sources and multiple relays. The relays use a simple Amplify-and-Forward relaying mechanism. For such networks, we formulate the max-min and sum capacity optimization problems. The formulated optimization problems are non-convex and nonlinear in nature. We obtain the optimal solution of the optimization problems by using a known technique called global optimization algorithm (GOP). We note that the computational complexity of the GOP algorithm grows exponentially with the number of relays. Therefore, we propose low-complexity heuristics that provide suboptimal solutions to the given optimization problems. The simulation results show that the performance of the heuristics is close to that of the respective optimal solutions.

关键词： Power Allocation Cognitive Radio Two Way Relay global optimization algorithm

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Application of data mining in a global optimization algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2013年 66卷 24-33页

作者： Chen, T. Y. Huang, J. H. Natl Chung Hsing Univ Dept Mech Engn Taichung 40227 Taiwan

A hybrid global optimization algorithm is developed in this research. The probability of finding the global optimal solution is increased by reducing the search space. The activities of classification, association, and clustering in data mining are employed to achieve this purpose. The hybrid algorithm developed uses data mining (DM), evolution strategy (ES) and sequential quadratic programming (SQP) to search for the global optimal solution. For unconstrained optimization problems, data mining techniques are used to determine a smaller search region that contains the global solution. For constrained optimization problems, the data mining techniques are used to find the approximate feasible region or the feasible region with better objective values. Numerical examples demonstrate that this hybrid algorithm can effectively find the global optimal solutions for two benchmark test problems. (C) 2013 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

关键词： global optimization algorithm Data mining Evolution strategy Sequential quadratic programming Reduced search space Hybrid search method

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Torque ripple reduction for switched reluctance motors using global optimization algorithm

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JOURNAL OF POWER ELECTRONICS 2022年第11期22卷 1897-1907页

作者： Ben, Tong Nie, Heng Chen, Long Jing, Libing Yan, Rongge China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China China Three Gorges Univ Ctr Power Transmiss Lines Hubei Prov Engn Technol Res Yichang Peoples R China Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China

global optimization algorithms are widely used to effectively suppress the torque ripple in switched reluctance motors (SRMs). In this paper, an improved velocity-controllable particle swarm optimization algorithm (VCPSO) is proposed to optimize the turn-off angle of an SRM under the current chopping control (CCC) method. In addition, the performances of three global optimization algorithms are compared and analyzed. The specific steps are outlined as follows. First, the static non-linear inductance-current-position and torque-current-position curves of the SRM are obtained through finite-element calculations, and a non-linear model of the SRM is established on this basis. Second, the turn-off angle optimization method based on the VCPSO is proposed. Finally, the performances of the simulated annealing algorithm (SA), the genetic algorithm (GA), and the proposed algorithm are compared and analyzed in terms of torque ripple suppression. The obtained results show that the proposed VCPSO has the advantages of a low number of iterations, low torque ripple, and small peak current in the torque ripple reduction issue of the SRM.

关键词： Switched reluctance motor Torque ripple Turn-off angle optimization global optimization algorithm

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Design of URAs by DIRECT global optimization algorithm

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OPTIK 2009年第8期120卷 370-373页

作者： Lang, Haitao Liu, Liren Yang, Qingguo Beijing Univ Chem Technol Beijing 100029 Peoples R China Chinese Acad Sci Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China

Coded aperture imaging (CAI) has evolved as a standard technique for imaging high-energy photon sources and has found numerous applications. Coded aperture arrays (CAAs) are the most important devices in the applications of CAI. In recent years, many approaches were presented to design optimum or near-optimum CAAs. Uniformly redundant arrays (URAs) are the most successful CAAs for their cyclic autocorrelation consisting of a sequence of delta functions on a flat sidelobe, which can easily be subtracted when the object has been reconstructed. Unfortunately, the existing methods can only be used to design URAs with limited number of array sizes and fixed autocorrelative sidelobe-to-peak ratio. In this paper, we presented a method to design more flexible URAs by means of a global optimization algorithm named DIRECT. By our approaches, we obtain various types of URAs including the filled URAs which can be constructed by existing methods and the sparse URAs which have never been constructed and mentioned by existing papers as far as we know. (C) 2007 Elsevier GmbH. All rights reserved.

关键词： Coded aperture arrays Filled URAs Sparse URAs global optimization algorithm DIRECT

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Porcine automation: Robotic abdomen cutting trajectory planning using machine vision techniques based on global optimization algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 143卷 193-200页

作者： Liu, Yi Cong, Ming Zheng, Huadong Liu, Dong Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

The purpose of this paper is to provide details on implementation of accurate and intelligent automation solution for porcine abdomen cutting while a pig is hung up by rear legs. The system developed utilized a 6-DOF industrial manipulators, customized tools, 2D camera and PC. Eye-to-hand calibrations built coordinate transformation relations of units in Cartesian space. The porcine abdomen curve was identified and fitted into quintic spline curve from image. Under cavum peritonaei constrains, optimal sectional trajectory was planned based on genetic algorithm (GA) by comparing several kinds of optimization algorithms. The results of experimental replications show that the system was successful both in following the varied position carcass and cutting open abdominal cavity without haslet damage. The system can enhance the quality, hygienic standard and efficiency of the process.

关键词： Machine vision Trajectory planning Industrial robot Porcine automation global optimization algorithm

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A global optimization algorithm for solving a four-person game

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optimization LETTERS 2019年第3期13卷 587-596页

作者： Batbileg, S. Tungalag, N. Anikin, A. Gornov, A. Finkelstein, E. Natl Univ Mongolia Ulaanbaatar Mongolia RAS Matrosov Inst Syst Dynam & Control Theory SB Irkutsk Russia

The nonzero sum four-person game was considered. We show that the game can be reduced to a global optimization problem by extending Mills' result (J Soc Ind Appl Math 8(2):397-402, 1960). For solving the problem, we propose a global optimization method that combines the ideas of the classical multistart and an estimation of a convexity degree of the function. The proposed algorithm was tested numerically on some problems created by the well-known generator GAMUT (GAMUT is a Suite of Game Generators. http://***) and allowed us to find solutions to the four-person game.

关键词： Nash equilibrium Nonzero sum game Mixed strategies global optimization algorithm

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Novel global optimization algorithm with a Space-Filling Curve and Integral Function

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Journal of the Operations Research Society of China 2021年第3期9卷 619-640页

作者： Zhong-Yu Wang Yong-Jian Yang Department of Mathematics Shanghai UniversityShanghai 200444China

In this study,we consider the global optimization problem in a *** use a class of series to construct a curve in a hypercube,which can fill the hypercube,and we present an integral function on the *** on the integral function,we propose an algorithm for solving the global optimization ***,we perform a convergence analysis and numerical experiments to demonstrate the effectiveness of the proposed algorithm.

关键词： global optimization algorithm Integral function Space-filling curve Filled function

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Structural analysis of carbon clusters by using a global optimization algorithm with Brenner potential

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JOURNAL OF MOLECULAR STRUCTURE-THEOCHEM 2004年第1-3期678卷 113-122页

作者： Cai, WS Shao, N Shao, XG Pan, ZX Univ Sci & Technol China Dept Chem Hefei 230026 Anhui Peoples R China

A global optimization algorithm for generation of the carbon cluster structures was developed based on the parallel fast annealing evolutionary algorithm with Brenner potential. Using the proposed algorithm, the structures with the lowest energies of the carbon clusters C-N with N from 2 to 71 were successfully obtained. It was found that the geometry is linear for the carbon clusters with N less than 5, mono-ring for the carbon clusters with N = 5-17, and fullerene-like cage or fullerene for the carbon clusters with N greater than 17. Furthermore. the factors affecting the stability of the carbon clusters were analyzed. It was found that apart from the number of the bonds, the symmetry of the structure. and the number of the adjacent pentagons, the curvature of the cage surface and the distribution of the adjacent pentagons are also key factors. (C) 2004 Elsevier B.V. All rights reserved.

关键词： carbon clusters global optimization algorithm Brenner potential lowest energy structures

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3S optimizer: a new meta-heuristic global optimization algorithm

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3535-3552页

作者： Li, Yanjun China South Ind Grp Corp Automat Res Inst Co Ltd Mianyang Peoples R China Sichuan Univ Chengdu Peoples R China

In this research, an original algorithm called 3S optimizer (3SO) is presented, which is inspired by swarm intelligence (SI) techniques. This novel proposed 3SO algorithm takes both the local exploitation and the global exploration into consideration, so it has satisfactory convergence speed and good solution accuracy. In order to evaluate the search ability of 3SO algorithm, this paper uses a set of famous test functions (including 23 functions of CEC-BC-2005 and 29 test functions of CEC-BC-2017). It proves that the 3SO algorithm has a great competitiveness with the most advanced optimization methods in terms of solution accuracy, convergence speed and stability from the experimental results. In order to fully demonstrate the practical application potential of the new technique, the 3SO algorithm is successfully applied to four engineering design problems. The simulation results show that the proposed 3SO algorithm can effectively deal with practical application problems.

关键词： 3SO algorithm Swarm intelligence global optimization algorithm Meta-heuristic Engineering applications

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