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检索条件"主题词=Local Search Algorithm"

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Lattice-Reduction Aided Optimum and Sub-Optimum SIC Detection for MIMO Systems 82

Lattice-Reduction Aided Optimum and Sub-Optimum SIC Detectio...

引用

IEEE 82nd Vehicular Technology Conference (VTC Fall)

作者： Lim, Sung-Ho Choi, Ji-Woong DGIST Dept Informat & Commun Engn Daegu South Korea

ISBN: (纸本)9781479980918

Lattice reduction (LR) has been researched a lot in conjunction with ordered successive interference cancellation (OSIC), contributing large enhancement of the performance with relatively small complexity. However, the performance gap between the LR-aided OSIC (LR-OSIC) and maximum likelihood (ML) still exists. This paper suggests LR-aided all-ordering successive interference cancellation (LR-AOSIC) as an optimum successive interference cancellation (SIC) detector showing near-ML performance almost unaffected by channel correlation and modulation order. In addition, LR-OSIC using modified local search algorithm (LR-MLS-OSIC) is also proposed as a sub-optimum SIC detector to reduce the complexity of LR-AOSIC affordably, nearly maintaining the performance.

关键词： spatial multiplexing MIMO (SM MIMO) successive interference cancellation lattice reduction local search algorithm

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Planning Method of Near-Minimum-Time Task Tour for Industrial Point-to-Point Robot 9

Planning Method of Near-Minimum-Time Task Tour for Industria...

引用

9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / IEEE Conference on Robotics, Automation and Mechatronics (RAM)

作者： Nemoto, Kosuke Aiyama, Yasumichi Univ Tsukuba Dept Intelligent Interact Technol Tsukuba Ibaraki Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki Japan

ISBN: (数字)9781728134581

ISBN: (纸本)9781728134581

This paper introduces a near-optimal path planning for industrial robot for multiple point tasks. For instance, spot welding, drilling, screwing and inspection with camera are popular multiple point work for industrial robots. How to decide the sequence of robot motions is important issue for factory automation because it directory influences to system productivity. Though getting optimal tour is not easy because this is kind of Traveling Salesman Problem (TSP) that is almost impossible to solve in polynomial time. In TSP the cost between two cities is Oven by geometrical distance though in robotics time for moving between two task points is gets cost. This article adopts heuristic algorithm that is often used to solve TSP to solve about 50 task points path planning for industrial robot. As a result, the algorithm gives under PA gap solution against best known solutions. For actual robot tasks, it generates about 6 to 10% efficient results compared with human task planner's path.

关键词： traveling salesman problem industrial robot time optimization local search algorithm

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Phase-only Direct Data Domain Adaptive Processing algorithm using Hybrid Genetic algorithms

Phase-only Direct Data Domain Adaptive Processing Algorithm ...

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14th IEEE International Conference on Communication Technology (ICCT)

作者： Zhu, Hu Deng, Lizhen Bai, Xiaodong Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Elect & Elect Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781467321013;9781467321006

A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is presented. Considering practical application of the complex weight requires complex hardware system, the new algorithm phase-only adaptive processing based on direct data domain is presented to reduce the complexity requirement of system implementation. Then a hybrid genetic algorithms combined genetic algorithm with local search algorithm is used to solve the phase-only adaptive weights. Experimental results show that the phase-only weights solved by a hybrid genetic algorithm not only form deep null in the direction of interferences, but also estimate the magnitude of the target signal accurately. The proposed algorithm achieves the purpose of interference suppression and detecting targets, as well as reduces the complexity of hardware implementation of the system greatly for engineer implement easily.

关键词： phase-only direct data domain genetic algorithms local search algorithm

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Two Hybrid Heuristic algorithms for Optimizing Data Processing Schedules in Distributed Edge Computing 11

Two Hybrid Heuristic Algorithms for Optimizing Data Processi...

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11th International Conference on Wireless Networks and Mobile Communications (WINCOM) - Inclusive and Intelligent Connectivity

作者： Sayah, Achraf Aqil, Said Aadil, Bouchra Lahby, Mohamed Hassan II Univ Lab Appl Math & Comp Sci Higher Normal Sch Casablanca Morocco Hassan II Univ Lab Artificial Intelligence & Complex Syst Engn Natl Higher Sch Arts & Crafts Casablanca Morocco

ISBN: (纸本)9798350377873;9798350377866

In recent years, edge computing has emerged as a promising solution in the field of network computing. This architecture ensures the availability of distributed computing resources located closer to end-users and IoT devices. However, resource scheduling remains a significant challenge in edge computing, requiring effective strategies to optimize resource utilization and ensure efficient task allocation. In this paper, we propose two hybrid approaches that combine the NawazEnscore-Ham (NEH) algorithm with local search and Greedy Random Adaptive search Procedure (GRASP) algorithm with local search for modeling and solving data traffic in distributed edge computing environments (DPSDEC). Through extensive evaluations, we consistently observe that the NEH algorithm outperforms GRASP, delivering minimized makespan and generating efficient schedules. Moreover, the NEH algorithm performs very well in less complex situations and maintains this advantage even in larger and more complex problems.

关键词： local search algorithm Edge Computing Distributed Networks Scheduling.

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On a Wireless Sensor Network Problem with Spanning Tree Backbone 18

On a Wireless Sensor Network Problem with Spanning Tree Back...

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18th International Conference on Wireless Networks and Mobile Systems (WINSYS)

作者： Adasme, Pablo Firoozabadi, Ali Dehghan Univ Santiago Chile Dept Elect Engn Ave Ecuador 3519 Santiago Chile Univ Tecnol Metropolitana Dept Elect Av Jose Pedro Alessandri 1242 Santiago 7800002 Chile

ISBN: (纸本)9789897585296

Let G = (V, E) be a complete graph with set of nodes V = {1, . . .,} g and edge set E = {1, . . ., m} representing a wireless sensor network. In this paper, we consider the problem of finding a minimum cost spanning tree backbone formed with p is an element of Z(+) out of n nodes where p < n in such a way that the n - p remaining nodes of G are connected to the leaf nodes of the backbone structure at minimum connectivity cost. Notice that this problem arises as a combination of two classical combinatorial optimization problems, namely the p-Median and spanning tree problems. We propose two mixed-integer linear programming (MIP) formulations for this problem as well as a local search heuristic. The proposed models and algorithm can be used as a reference source for comparison purposes when designing future network protocols. We consider complete graph instances with Euclidean and random uniform costs. Our preliminary numerical results indicate that one of the proposed models performs slightly better than the other one in terms of solution quality and CPU times obtained with the Gurobi solver. Finally, the proposed heuristic allows one to obtain near-optimal solutions in remarkably less CPU time compared to the MIP models.

关键词： Combinatorial Optimization p-Median and Spanning Tree Problems Mixed-integer Linear Programming Wireless Sensor Networks local search algorithm

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Solving Lot-streaming Flow Shop Scheduling Problems Using a Discrete Harmony search algorithm

Solving Lot-streaming Flow Shop Scheduling Problems Using a ...

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2010 IEEE World Congress on Computational Intelligence

作者： Pan, Quan-Ke Tasgetiren, Mehmet Fatih Suganthan, Ponnuthurai Nagaratnam Liang, Yun-Chia Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan

ISBN: (纸本)9781424481262

The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm, a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm based on the insert neighborhood structure is fused to stress the further enhancement capability of the algorithm proposed whereas a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.

关键词： discrete harmony search algorithm evolutionary computation evolutionary computation techniques flow shop scheduling insert neighborhood structure job permutations representation job sequences local search algorithm lot-streaming flow shop scheduling problems optimisation optimization problems search problems

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Shrinking Hyper-Sphere based Artificial Bee Colony algorithm

Shrinking Hyper-Sphere based Artificial Bee Colony Algorithm

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International Conference on Signal Propagation and Computer Technology (ICSPCT)

作者： Hussain, Ashik Gupta, Shruti Singh, Rohit Trivedi, Prakriti Sharma, Harish Govt Engn Coll Ajmer Ajmer India Career Point Univ Kota Rajasthan India Vardhaman Mahaveer Open Univ Kota India

ISBN: (纸本)9781479931408

Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of other swarm based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. Therefore, to balance the exploration and exploitation characteristics of the ABC, Shrinking Hyper-Sphere based local search approach is developed and hybridized with in the ABC solution search process. The proposed algorithm is named as Shrinking Hyper-Sphere based ABC (SHABC). The experiments over 14 well known benchmark functions of complex in nature, show that the SHABC algorithm perform better than the original version of ABC and its latest variant, namely Modified ABC (MABC) in most of the experiments.

关键词： Swarm intelligence Evolutionary computation local search algorithm Shrinking Hyper-Sphere

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A Feature Selection algorithm to Find Optimal Feature Subsets for Detecting DoS Attacks 5

A Feature Selection Algorithm to Find Optimal Feature Subset...

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5th International Conference on IT Convergence and Security (ICITCS)

作者： Kang, Seung-Ho Dongshin Univ Dept Informat Secur Naju South Korea

ISBN: (纸本)9781467365376

The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires extensive computing resources. To tackle this problem we propose an optimal feature selection algorithm based on a local search algorithm. In order to evaluate the performance of our proposed algorithm, comparisons with a feature set composed of all 41 features are carried out over the NSL-KDD data set using a multi-layer perceptron.

关键词： intrusion detection system feature selection local search algorithm wrapper method NSL-KDD data set

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Indicator Weighted Based Multi-Objective Approach using Self-Adaptive Neighborhood Operator 25

Indicator Weighted Based Multi-Objective Approach using Self...

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25th KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)

作者： BenMansour, Imen Alaya, Ines Tagina, Moncef Univ Manouba ENSI LARIA Manouba Tunisia Esprit Sch Engn Tunis Tunisia

Balancing diversity and convergence seems to be a difficult task when solving multi-objective optimization problems (MOPs). For addressing this issue, researchers' interest has been drawn to hybrid approaches since this cooperation allows benefiting from the advantages of both approaches and gains better trade-offs overall. Considering such fact, this paper aims at introducing a hybrid approach as a synergy of a multi-directional Ant Colony Optimization algorithm with a local search method based on a weighted version of epsilon Indicator using a self-adaptive neighborhood operator coined as Indicator Weighted Based local search with Ant Colony Optimization (IWBLS/ACO) to handle the knapsack problem within the multi-objective framework. In IWBLS/ACO, initial solutions are created by the ant colony. Then, the enhancement phase is ensured by the local search procedure. The algorithm is evolving based on different configurations of the epsilon quality indicator through different weight vectors. Moreover, we propose in this work, a novel self-adaptive neighborhood operator which changes automatically and dynamically as the IWBLS algorithm runs. The proposed IWBLS/ACO was tested on widely used Multi-objective Multidimensional Knapsack Problem (MOMKP) instances and compared with powerful state-of-the-art algorithms. Experimental results highlight that the proposed approach can lead to finding a good compromise between exploration and exploitation. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

关键词： Multi-objective optimization Ant Colony Optimization local search algorithm Weighted epsilon quality indicator Self-adaptive neighborhood operator

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Optimization of the power quality monitor number in smart grid

Optimization of the power quality monitor number in smart gr...

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2014 IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Wan, Yuxin Cao, Junwei Zhang, Huaying Zhu, Zhengguo Yao, Senjing Tsinghua Univ Dept Automat Res Inst Informat Technol Beijing 100084 Peoples R China Shenzhen Power Supply Co Ltd Shenzhen 518020 Peoples R China

ISBN: (纸本)9781479949342

One of the most important features in smart grid is power system self-healing and power quality improvement. Power quality monitoring is essential to realize this feature. Installing power quality monitors (PQM) in every component of the power network is not feasible due to economic reasons. So how to find the optimal number and locations of power quality monitors while maintaining system observability becomes an important problem. The major contribution of this paper includes providing the model for PQM optimization problem considering both system observability and fault location constraints. The model is then formulized as an integer linear problem and reduced to a group of k-median decision problems. A local search algorithm is proposed to solve the problem. The IEEE 14 bus network is utilized as a case study. algorithm efficiency is evaluated using Matlab tools and compared with an existing branch and bound algorithm. Experimental results show that proposed algorithm is more than an order of magnitude faster than current algorithm while maintain the accuracy of results.

关键词： smart grid power quality power quality monitor optimization fault location local search algorithm

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