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2nd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Ali, Ahmed Fouad Hassanien, Aboul Ella Snasel, Vaclav Suez Canal Univ Dept Comp Sci Fac Comp & Informat Ismailia Egypt Cairo Univ Fac Comp & Informat Cairo Egypt VSB Tech Univ Ostrava Sci Res Grp Egypt Ostrava Czech Republic

ISBN: (纸本)9783319134611;9783319134604

Due to the simplicity of the Artificial Bee Colony (ABC) algorithm, it has been applied to solve a large number of problems. ABC is a stochastic algorithm and it generates trial solutions with random moves, however it suffers from slow convergence. In order to accelerate the convergence of the ABC algorithm, we proposed a new hybrid algorithm, which is called Memetic Artificial Bee Colony for Integer Programming (MABCIP). The proposed algorithm is a hybrid algorithm between the ABC algorithm and a Random Walk with Direction Exploitation (RWDE) as a local search method. MABCIP is tested on 7 benchmark functions and compared with 4 particle swarm optimization algorithms. The numerical results demonstrate that MABCIP is an efficient and robust algorithm.

关键词： Artificial bee colony local search algorithm memetic algorithm integer programming problems

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Research on Control Strategy of Large-scale Electric Heat Pumps Considering Power Supply Safety

Research on Control Strategy of Large-scale Electric Heat Pu...

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International Conference on Power System Technology (POWERCON)

作者： Guo, Wenxin Yu, Kun Chen, Xingying Huang, Xiaoshu Gan, Lei Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Jiangsu Peoples R China

ISBN: (纸本)9781538664612

After the implementation of the "Electric power alternatives" scheme, large-scale electric heat pumps (EHPs) were used to replace the previous coal-fired boilers, which makes the distribution network appears heavy load, overload and even affected the normal power consumption of users. Based on the state-queueing control model, the following operation control of the EHP is carried out for the purpose of ensuring its safe operation: using an improved K-means clustering algorithm suitable for the control group of the EHPs, the EHPs with similar control characteristics are divided into a same control group and carrie out state-queueing control;A local search algorithm is used to optimize the load operation for the combined optimization of the sub-range operation mode of each EHP in each distribution area. An example is given to verify that the proposed operation control strategy can avoid overloading of the distribution network and ensure normal power supply and safe operation of the system when large-scale EHP loads are concentrated.

关键词： Electric power alternatives electric heat pump power supply security control strategy local search algorithm

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Design and FPGA implementation of high-speed square-root-raised-cosine FIR filters 10

Design and FPGA implementation of high-speed square-root-rai...

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10th IEEE Digital Signal Processing Workshop/2nd IEEE Signal Processing Education Workshop

作者： Zhang, WL Pan, CY Guo, XB Yang, ZX Tsing Hua Univ State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China

ISBN: (纸本)0780381165

A series of square-root-raised-cosine(SRRC) FIR filter with CSD coefficients were designed according to the local search algorithm based upon MiniMax error criteria. The simulation results of a baseband system show that two 13-tap SRRC FIR filters with a roll-off factor 0.6 only introduced about 6% peak distortion in the eye pattern. A bit-level pipeline architecture was used to realize the high sampling rate FIR filter. An additional tap with fixed input of 10 was added to the final stage of the filter to avoid carry ripple. Consequently, the critical path consists of only a single one-bit full adder and a pipeline register. The filter was implemented in an altera's FPGA: EP20K60EFC144-1 and the timing analyses results show that the sampling rate could be over 200MHz.

关键词： field programmable gate arrays search problems pipeline processing sampled data filters FIR filters minimax techniques adders FPGA implementation field programmable gate arrays high-speed square-root-raised-cosine FIR filters finite impulse response filters canonic-signed digit coefficients local search algorithm minimax error criteria baseband system roll-off factor bit-level pipeline architecture sampling rate one-bit full adder pipeline register EP20K60EFC144-1 timing analyses wireless communication systems

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Hybrid Nested Partitions Method for the Traveling Salesman Problem 1

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8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Zong, Decai Wang, Kangkang Changshu Inst Technol Coll Comp Sci & Engn Changshu Peoples R China Jiangsu Univ Sci & Technol Sch Math & Phys Zhenjiang Peoples R China

ISBN: (数字)9783642549243

ISBN: (纸本)9783642549243;9783642549236

The nested partitions method (NPM) is a global optimization method, which can be applied to solve many large-scale discrete optimization problems. The basic procedure of this method for solving the traveling salesman problem (TSP) was introduced. Based on the analysis and determination of the strategy of the four arithmetic operators of NPM, an improved NPM was proposed. The initial most promising region was improved by weighted sampling method;The historical optimal solution of every region was recorded in a global array;the 3-opt algorithm was combined in the local search for improving the quality of solution for every subregion;the improved Lin-Kernighan algorithm was used in the search for improving the quality of solution for surrounding region. Some experimental results of TSPLIB (TSP Library) show that the proposed improved NPM can find solutions of high quality efficiently when applied to the TSP.

关键词： Nested partitions method local search algorithm Lin-Kernighan algorithm 3-Opt algorithm Traveling salesman problem

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Feature Selection in Multi-label Classification based on Binary Quantum Gravitational search algorithm 26

Feature Selection in Multi-label Classification based on Bin...

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26th International Computer Conference of the Computer-Society-of-Iran

作者： Noormohammadi, Hojat Dowlatshahi, Mohammad Bagher Lorestan Univ Dept Engn Khorramabad Iran

ISBN: (纸本)9781665412414

Unlike a single-label supervisor dataset where each instance is assigned to one class label, in multi-label datasets, several class labels are assigned to each instance, which makes it difficult to build an accurate and comprehensive model from this dataset. In this study, a memetic algorithm for feature selection in a multi-label dataset is proposed. The principal innovation of this study is the offer of a novel local search algorithm which, in collaboration with binary quantum-inspired gravitational search algorithm (BQIGSA), forms the main framework of the proposed memetic algorithm. The main invention of the proposed local search algorithm is to build a number of neighbors for a solution using the prior knowledge vector and the posterior knowledge vector to select effective features and remove useless and irrelevant features. The results of implementing the proposed algorithm and comparing these results with similar works show that the proposed method in most cases leads to better results.

关键词： Multi-label feature selection Memetic algorithm local search algorithm Prior knowledge vector Posterior knowledge vector Gravitational search algorithm

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Balancing exploration and exploitation in particle swarm optimization on search tasking

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Research Journal of Applied Sciences, Engineering and Technology 2014年第12期8卷 1429-1434页

作者： Nakisa, Bahareh Rastgoo, Mohammad Naim Norodin, Md Jan Faculty of Information Science and Technology Universiti Kebangsaan Malaysia UKM Bangi Selangor 43600 Malaysia

In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems. © Maxwell Scientific Organization, 2014.

关键词： Exploration and exploitation local search algorithm particle swarm optimization search tasking

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FPGA Based Satisfiability Checking

FPGA Based Satisfiability Checking

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作者： Subramanian, Rishi Bharadwaj University of Cincinnati

学位级别：M.S.

The Boolean satisfiability problem, abbreviated as SAT, is the backbone of many applications in VLSI design automation and verification. Over the years, many SAT solvers, both complete and incomplete, have been developed. Complete solvers are usually based on the DPLL (Davis–Putnam–Logemann–Loveland) algorithm, which is a backtracking algorithm. Industrial strength problems are very large and make DPLL based solvers impractical for some applications. In such cases, local search algorithms that try to find a solution within a stipulated time can be used. These algorithms look at SAT problem as an optimization problem. They start with an initial random solution and explore a certain search space by iteratively making local changes to the solution using a greedy, heuristic algorithm to find a global optimum. Over the past few years, heterogeneous devices such as Graphics Processing Units (GPU) and Field Programmable Gate Arrays (FPGA) have been used to accelerate the SAT problem and handle large SAT instances. There has been a growing interest in exploiting the parallel and pipeline processing power of FPGAs for various applications. New process technologies have allowed for more logic blocks, memory elements, and faster FPGAs, making it a perfect candidate for parallel computing. This thesis presents a local search algorithm Walksat, implemented on the Xilinx Alveo U250 Accelerator card. The entire solver has been developed using the OpenCL framework. On-chip memory available on the FPGA has been exploited to a great extent and the solver can handle SAT problems of up to 98,000 variables and 401,800 clauses. We have also analyzed the performance of our solver against the state of the art complete and incomplete solvers.

关键词： Electrical Engineering FPGA Based SAT Solver Boolean Satisfiability local search algorithm SAT Solver

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A MIP model and a hybrid genetic algorithm for flexible job-shop scheduling problem with job-splitting

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COMPUTERS & OPERATIONS REsearch 2023年第1期155卷

作者： Tutumlu, Busra Sarac, Tugba Kutahya Dumlupinar Univ Fac Engn Dept Ind Engn TR-43100 Kutahya Turkiye Eskisehir Osmangazi Univ Fac Engn & Architecture Dept Ind Engn TR-26480 Eskisehir Turkiye

In the scheduling literature, it is generally assumed that jobs are not split into sub-lots, or that the number and size of sub-lots are limited or predetermined. These assumptions make the problem more manageable. However, they may prevent more successful schedules. For many businesses, considering the splitting of jobs while scheduling them can create significant improvement opportunities. This study addresses the Flexible Job-Shop Scheduling Problem (FJSP) with job-splitting, determining how many sub-lots each job should be split into and the size of each sub-lot. A MIP model is proposed for the considered problem. In the model, the size and number of sub-lots of a job are not predefined or bounded. The objective function of the model is to minimize the makespan. Feasible solutions could not be found for large-sized problems by the mathematical model. So, a Hybrid Genetic algorithm (HGA) is also proposed. In the proposed HGA, a local search algorithm (LSA) that determines the size of sub-lots has been included in the GA to improve the efficiency. To show the success of the proposed HGA, its performance is compared with the classical GA.

关键词： Flexible Job Shop Scheduling Problem Job-Splitting Mixed Integer Programming Hybrid Genetic algorithm local search algorithm

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New decoding algorithms for Hidden Markov Models using distance measures on labellings

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BMC BIOINFORMATICS 2010年第s1期11卷 S40-S40页

作者： Brown, Daniel G. Truszkowski, Jakub Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada

Background: Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries. Results: We give a set of algorithms to compute the conditional probability of all labellings "near" a reference labelling lambda for a sequence y for a variety of definitions of "near". In addition, we give optimization algorithms to find the best labelling for a sequence in the robust sense of having all of its feature boundaries nearly correct. Natural problems in this domain are NP-hard to optimize. For membrane proteins, our algorithms find the approximate topology of such proteins with comparable success to existing programs, while being substantially more accurate in estimating the positions of transmembrane helix boundaries. Conclusion: More robust HMM decoding may allow for better analysis of sequence features, in reasonable runtimes.

关键词： local search Hide Markov Model Transmembrane Helix local search algorithm State Path

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An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Han, Xue Han, Yuyan Zhang, Biao Qin, Haoxiang Li, Junqing Liu, Yiping Gong, Dunwei Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Hunan 410082 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the increase in production levels, a pattern of industrial production has shifted from a single factory to multiple factories, resulting in a distributed production model. The distributed flowshop scheduling problem (DPFSP) is of great research significance as a frequent pattern in real production activities. In this paper, according to real-world scenarios, we have added blocking constraints and sequence-dependent setup times (SDST) to the DFSP and proposed a distributed blocking flowshop scheduling problem with sequence-dependent setup times (DBFSP_SDST). In a distributed environment, the allocation of resources and utilization have become an urgent problem to be solved. In addition, scheduling problems related to resource conservation have also attracted increasing attention. Therefore, we study DBFSP_SDST and consider minimizing the energy consumption cost of the critical factory (critical factory is the factory with maximum energy consumption cost) under resource balance. To tackle this problem, an effective iterated greedy algorithm based on a learning-based variable neighborhood search algorithm (VNIG) is proposed. In VNIG, an efficient construction heuristic is well designed. Two different local searches based on the characteristics of the proposed problem are developed to enhance the local exploitation by neighborhood searching. A learning-based selection variable neighborhood search strategy is designed to avoid the solution trapping in local optima. By conducting extensive simulation experiments, the proposed VNIG shows superior performance compared with artificial chemical reaction optimization (CRO, 2017), the discrete artificial bee colony algorithm (DABC, 2018), the iterative greedy algorithm with a variable neighborhood search scheme (IGR, 2021), and the evolution strategy approach (ES, 2022).(c) 2022 Elsevier B.V. All rights reserved.

关键词： Distributed flowshop scheduling problem Blocking Energy consumption cost local search algorithm Variable neighborhood search

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