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hybrid algorithm FOR THE COMPUTATION OF THE MATRIX POLYNOMIAL I+A+CENTER-DOT-CENTER-DOT-CENTER-DOT+A(N-1)

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-FUNDAMENTAL THEORY AND APPLICATIONS 1995年第7期42卷 377-380页

作者： DIMITROV, V COOKLEV, T TOKYO INST TECHNOL DEPT PHYS ELECTRTOKYO 152JAPAN

In this brief, a new method for computing the matrix polynomial is proposed. The comparison demonstrates that this is the most efficient technique at the moment, Furthermore we argue that the approach is optimal in the framework of the discussed algorithms. The algorithm is recursive and it can be viewed as resulting from a new number system.

关键词： computational complexity matrix multiplication polynomial matrices hybrid algorithm matrix polynomial number system recursive algorithm Recursive algorithms AN 1 counting system algorithms complexity classes Polynomial matrices Matrix multiplication

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hybrid algorithm FOR TRACKING MAXIMUM POWER IN SOLAR PV ARRAY UNDER PARTIALLY SHADED CONDITION

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INTERNATIONAL JOURNAL OF POWER AND ENERGY SYSTEMS 2019年第3期39卷 166-176页

作者： Vetrivelan, Balaji Kareem, Peer Fathima Abdul Vellore Inst Technol VIT Sch Elect Engn Chennai 600127 Tamil Nadu India

In this work, a novel hybrid global maximum power point tracking algorithm for tracking global maximum power of solar photovoltaic (PV) system under partial shading and dynamic irradiation condition is proposed. The developed hybrid algorithm [whale optimization (WO) and perturb and observe (PO)] is a combination of the recently introduced WO algorithm and the classical PO algorithm. During partially shaded conditions, the single-peak nonlinear power voltage (P-V) characteristic curve of a PV panel is scattered into multiple peaks consisting of many local peaks and a unique global peak with maximum power. In the proposed algorithm the WO is utilized for finding the global peak and the PO algorithm is used for finding the exact maximum power available in the global peak. The advantages of the proposed algorithm are high tracking accuracy, faster convergence, easy to implement, and it can work efficiently under uniform and partially shaded condition. The performance of the algorithm has been compared with PO, particle swarm optimization (PSO) and hybrid PSOPO using MATLAB simulations. The steady state and dynamic performance of the proposed algorithm are verified by simulating it with a complex set of shading patterns, and the results show the efficiency greater than 99% and fast convergence towards maximum power.

关键词： Photovoltaic (PV) system global maximum power point tracking (GMPPT) partially shaded condition whale optimization (WO) algorithm hybrid algorithm

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hybrid algorithm Combing Genetic algorithm With Evolution Strategy for Antenna Design

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IEEE TRANSACTIONS ON MAGNETICS 2016年第3期52卷 7209004-1-7209004-4页

作者： Choi, Kyung Jang, Dong-Hyeok Kang, Seong-In Lee, Jeong-Hyeok Chung, Tae-Kyung Kim, Hyeong-Seok Kangwon Natl Univ Dept Elect Engn Chunchon 200701 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES in order to make up for these defects. First, we reached the vicinity of optimal solution using the GA. Then, the ES is used to find the accurate optimal solution. The switching point can be a main issue, which is also resolved in this paper. First, the performance of the convergence speed and the solution accuracy are comparatively tested using the known functions. In addition, the optimized design of the 2.45 GHz coplanar waveguide-fed circularly polarized antenna is carried out as a practical application. Only the GA and the hybrid algorithm reach the satisfactory value, and the more rapid convergence can be shown by the ES in this hybrid method after 380 iterations.

关键词： Coplanar waveguide (CPW)-fed circularly polarized antenna evolution strategy (ES) genetic algorithm (GA) hybrid algorithm

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A hybrid algorithm for the design of DOE in uniform illumination

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OPTICS COMMUNICATIONS 2000年第4-6期181卷 261-265页

作者： Wang, W Li, T Li, YP Univ Sci & Technol China Dept Phys Hefei 230026 Peoples R China USTC Natl Synchrotron Radiat Lab Hefei 230027 Peoples R China

A kind of hybrid algorithm for diffractive optical elements (DOE) design is described. The pure phase plates (PPE) simulated by this method homogeneously concentrate more than 97% of the incident energy into the desired focal-plane region. The intensity focal-plane profile has an almost perfectly flat top. Its fit to the required profile measured by the mean square error is 0.15%. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： DOE hybrid algorithm optimization

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A learning-guided hybrid genetic algorithm and multi-neighborhood search for the integrated process planning and scheduling problem with reconfigurable manufacturing cells

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 93卷

作者： Hu, Yiwen Dong, Hongliang Liu, Jianhua Zhuang, Cunbo Zhang, Feng Beijing Inst Technol Sch Mech Engn Lab Digital Mfg Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401151 Peoples R China Southwest China Res Inst Elect Equipment Chengdu 610000 Peoples R China

Integrated process planning and scheduling (IPPS) is a crucial component of an intelligent manufacturing system. While most existing studies have focused on the manufacturing workshop, less attention has been given to the assembly and test workshops, which typically include reconfigurable manufacturing cells (RMCs). Therefore, this paper focuses on IPPS with reconfigurable manufacturing cells (IPPS_RMCs) in the context of assembly and test workshops. The objective of IPPS_RMCs is to minimize the makespan and total weighted tardiness, taking into account priority constraints and capability conversion limits of RMCs. To address and optimize this problem, a learning-guided hybrid genetic algorithm (LG_HGA) is proposed, which utilizes chromosome encoding to solve the process planning and scheduling problem synchronously. The LG_HGA incorporates NSGA-II as the global search and employs a learning-guided multi-neighborhood search (LG_MNS) to achieve a better balance between exploration and exploitation. In the global search phase, a problem-based methodology for gene operation is introduced. The LG_MNS consists of four neighborhood structures, based on critical paths and heuristic rules. Additionally, the learning-guided mechanism involves using a decision tree regression model to learn data from the knowledge base and determine how to perform local search. Through case tests of various sizes, the experimental results demonstrate that LG_HGA outperforms several advanced multi-objective evolutionary algorithms due to the proposed improved genetic operations, neighborhood structure, and learning mechanism.

关键词： Integrated process planning and scheduling Reconfigurable manufacturing cells hybrid algorithm Neighborhood structure Learning-guided mechanism

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hybrid algorithm for acceleration of convergence to Cyclic Steady State

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Journal of Applied Sciences 2009年第17期9卷 3205-3206页

作者： Lai, Y.L. Choong, T.S.Y. Chuah, T.G. Ahamad, I.S. Razon, L.F. School of Engineering Faculty of Engineering and Built Environment SEGi University College Kota Damansara Malaysia Department of Chemical and Environmental Engineering Faculty of Engineering Universiti Putra Malaysia 43300 Serdang Selangor Malaysia Department of Chemical Engineering De La Salle University 2401 Taft Avenue 1004 Manila Philippines

Cyclic process is inherently dynamic, thus it has no steady state. However, the process, after a sufficient number of cycles, will reach a state called Cyclic Steady State (CSS) where the process state variables at some instant within a cycle have the same value at the corresponding instant within each subsequent cycle. Depending on the nature of the cyclic process, the number of cycles needed before the process reaches CSS may vary from tens to thousands. In this study, a hybrid algorithm that aims to predict and accelerate the convergence of the CSS is developed and tested on a cyclic operation of controlled-cycled stirred tank reactor. © 2009 Asian Network for Scientific Information.

关键词： hybrid algorithm prediction of CSS acceleration of CSS

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hybrid algorithm Combining Ant Colony algorithm with Genetic algorithm for Continuous Domain

Hybrid Algorithm Combining Ant Colony Algorithm with Genetic...

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9th International Conference for Young Computer Scientists

作者： Liu, Bo Meng, Peisheng Huazhong Univ Sci & Technol Dept Mech Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781424441983

Ant Colony algorithm is a kind of new heuristic biological modeling method which has the ability of parallel processing and global searching. Ay use of the properties of Ant Colony algorithm and Genetic algorithm, the hybrid algorithm which adopts Genetic algorithm to distribute the original pheromone is proposed to solve the continuous optimization problem. Several solutions are obtained using the Ant Colony algorithm through pheromone accumulation and renewal. Finally, by using crossover and mutation operation of Genetic algorithm, some effective solutions are obtained The results of experiments show better performances of the new algorithm based on six continuous test functions compared with the methods available in literature.

关键词： hybrid algorithm Ant Colony algorithm (ACA) Genetic algorithm (GA) continuous optimization

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hybrid algorithm for Multi-objective Optimization of PMSM using massively distributed Finite Element Analysis

Hybrid Algorithm for Multi-objective Optimization of PMSM us...

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12th International Conference on Optimization of Electrical and Electronic Equipment

作者： Krotsch, Jens Piepenbreier, Bernhard Univ Erlangen Nurnberg Chair Elect Drives & Machines Erlangen Germany

ISBN: (纸本)9781424470198

A hybrid multi-objective optimization algorithm based on partial aspects of evolution strategy combining stochastic and deterministic elements with the aim of a high efficiency and high scalability suitable for massively distributed finite element analysis is investigated. The selection and generation process of solution candidates depend on density in design variable and objective space. New solution candidates are generated by stochastic variation but also systematically using a triangular grid. With respect to effective design analysis an approximation method is used. The proposed algorithm is evaluated using test cases and is successfully applied to dimension and shape optimization of a permanent magnet synchronous motor.

关键词： Multi-objective optimization hybrid algorithm grid-based design generation distributed analysis objective value approximation permanent magnet motor design optimization cogging-torque reduction

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hybrid algorithm for Line Planning Problem

Hybrid Algorithm for Line Planning Problem

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International Symposium on Computational and Business Intelligence (ISCBI)

作者： Ratajczak, Piotr Michaluszko, Malgorzata Polak, Dawid Koszalka, Leszek Pozniak-Koszalka, Iwona Kasprzak, Andrzej Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9780769550664

The line planning problem belongs to the area of the public transportation strategic planning. In this paper, we propose a new hybrid algorithm dealing with this problem. Its performance is verified against a simulated annealing algorithm and random search. The obtained results are promising;the hybrid algorithm has produced good solutions in almost all of tested scenarios.

关键词： line planning problem public transport hybrid algorithm evolutionary algorithm experimentation system

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hybrid algorithm: fuzzy logic-genetic algorithm on traffic light intelligent system

Hybrid algorithm: fuzzy logic-genetic algorithm on traffic l...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Odeh, Suhail M. Bethlehem Univ Comp & Informat Syst Dept Bethlehem Palestine

ISBN: (纸本)9781467374286

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic algorithms (GA) and its application on a traffic light system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing and forecasting. They are, essentially, ruled-based systems, in which the definition of these rules and fuzzy membership functions is generally base on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it was use to adapt the decision rules of FLCs that define an intelligent traffic light system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic light controller - Conventional traffic controller (CTC) -, and up to 31% in the comparison with a traditional logic FLC Controller.

关键词： Fuzzy Logic Genetic algorithm Conventional traffic controller hybrid algorithm Intelligent traffic light system

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