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heuristic genetic algorithm parameter optimizer: Making lossless compression algorithms efficient and flexible

EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Wang, Weijie Chen, Wenhui Yan, Li Yang, Yanqing Zhao, Huihuang Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421002 Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & App Hengyang 421002 Peoples R China Hunan Univ Natl Engn Lab Robot Visual Percept & Control Techn Hengyang Peoples R China

The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floatingpoint lossless compression algorithms often struggle to deliver exceptional performance on diverse datasets, highlighting their inherent limitations. To address this issue, we propose a novel method called the heuristic genetic algorithm Parameter Optimizer for Lossless Compression of Time Series Floating Point Data (HGAACTF). This method features a highly effective parameter optimizer designed specifically for compression algorithms that utilize leading zeros. The combination of our parameter optimizer and the HGA-ACTF algorithm strategy has been proven to outperform existing leading compression algorithms across multiple fields. This approach not only enhances the compression ratio but also significantly reduces both compression and decompression times. In our comparative study, we evaluated the HGA-ACTF algorithm against eleven well- performing algorithms and a variant of the algorithm, integrating our parameter optimizer and algorithmic strategy into other adaptable algorithms, and demonstrating notable improvements. Experimental results indicate that the HGA-ACTF algorithm achieves an average compression ratio improvement of 38.87%, with some datasets showing improvements of up to 54.36%. Our approach effectively addresses the transmission and storage of time series data, significantly reducing the overhead associated with data processing. The code can be found at https://***/wwj10/HGA-ACTF.

关键词： Time series floating point data Lossless compression Internet of things Compression algorithm heuristic genetic algorithm

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A heuristic genetic algorithm for the single source shortest path problem

A heuristic genetic algorithm for the single source shortest...

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5th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA-07)

作者： Hasan, Basela S. Khamees, Mohammad A. Mahmoud, Ashraf S. Hasan Arab Open Univ Fac Comp & Informat Technol Amman Jordan Al Israa Private Univ Dept Basic Sci Amman Jordan King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781424410309

This paper addresses one of the potential graph-based problems that arises when an optimal shortest path solution, or near optimal solution is acceptable, namely the Single Source Shot-test Path (SSP) problem. To this end, a novel heuristic genetic algorithm (HGA) to solve the SSSP problem is developed and evaluated. The proposed algorithm employs knowledge front deterministic techniques and the genetic mechanism to achieve high performance and allow consistent convergence. In addition, the proposed HGA is implemented and evaluated using a developed software tool that is easily amenable for future extensions and variations of our HGA. The schema introduced in this proposal depends on starting with initial population of candidate solution paths constraints as an alternative of a randomly generated one. To preserve the high performance candidate solutions, the HGA also uses a new heuristic order crossover (HOC) operator and mutation (HSM) operator to keep the search limited to feasible search domain. Simulation results indicate that the developed HGA is highly efficient in finding an optimal also quantify the effect initial population size and the increase of generation numbers.

关键词： single source shortest path problem heuristic genetic algorithm dijkstra's algorithm

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A heuristic genetic algorithm for Multi-Mode Resource-Constrained Project Scheduling Problem

A Heuristic Genetic Algorithm for Multi-Mode Resource-Constr...

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2010 3rd IEEE International Conference on Computer Science and Information Technology—ICCSIT 2010

作者： Li Zheng 1 Qin Jinlei 2 Zhang Changming 1 1.Department of Computer Science and Technology North China Electric Power University (Baoding) Baoding,China 2.Information and Network Management Center North China Electric Power University (Baoding) Baoding,China

A heuristic genetic algorithm for multi-mode resource-constrained project scheduling problem is given because the requisition of constraints of using renewable and nonrenewable resource in practical engineering is taken into *** of nonrenewable resource,weighted coefficient and the duration are combined,and objective function is constructed dynamically by choosing different *** this way,the optimal solution of scheduling mode can be gained which meets different needs of *** has been proved that by adopting the algorithm,not only duration can be the shortest,but also nonrenewable resources can be saved maximum,which will be great significance to achieve the optimal arrangement of time and resources in practical production.

关键词： heuristic genetic algorithm weight multi-mode resource-constrained project scheduling scheduling pattern sequence

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Integrated optimisation of scheduling and ordering based on semi-finished goods delayed differentiation

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第16期51卷 4805-4819页

作者： Ni, Yanting Wang, Yi Yao, Jin Li, Jingmin Sichuan Univ Sch Mfg Sci & Engn Chengdu 610065 Peoples R China

The implementation of dynamic ordering policies is becoming increasingly important for the competitiveness of modern manufacturing systems. However, existing models on dynamic ordering pay little attention to production scheduling, which greatly affects the fulfilment of dynamic ordering, especially in complex manufacturing systems. Therefore, it is imperative to establish a new model which integrates both dynamic ordering and production scheduling. Accordingly, a quantitative measurement method for integration is needed. To this end, this paper proposes a semi-finished goods delayed differentiation (SFGDD) model by taking into account integration of the scheduling inventory control and dynamic ordering simultaneously. The objective of this model is to study the relationship between the shop floor inventory and the ordering control based on the semi-finished goods dynamic dispatching mechanism. In addition, the days of inventory (DOI) and a backorder penalty exponential function are developed to quantitatively measure such a relationship. To obtain the optimal results, this paper employs a heuristic genetic algorithm (HGA) with a heuristic encoding scheme to synchronise the generation and selection of inventory variables coherently. A case study on a semiconductor assembly and test manufacturing (ATM) is presented, and a significant revenue enhancement and inventory reduction are achieved accordingly.

关键词： dynamic ordering scheduling heuristic genetic algorithm inventory control

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Development of a re-configurable ambisonic decoder for irregular loudspeaker configuration

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IET CIRCUITS DEVICES & SYSTEMS 2009年第4期3卷 197-203页

作者： Tsang, P. W. M. Cheung, K. W. K. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

This study reports a heuristic genetic algorithm to determine the decoding parameters in a first-order ambisonic system for reconstructing a three-dimensional sound field with an arbitrary quad speaker configuration. On this basis, a hardware prototype has been developed using a field programmable gate array (FPGA) to decode ambisonic signals that are encoded in the standard B-format. To allow direct coupling with digital audio sources, the input and output channels of the decoder are implemented with the 12S interface. Evaluations reveal that the decoding parameters derived by this method are superior to existing approaches in terms of flexibility in loudspeaker configuration and optimisation of some of the essential factors in surround sound reconstruction.

关键词： first-order ambisonic system loudspeakers heuristic programming heuristic genetic algorithm standard B-format acoustic signal processing irregular loudspeaker configuration decoding arbitrary quad speaker configuration field programmable gate array digital audio sources reconfigurable architectures re-configurable ambisonic decoder encoding decoding parameters field programmable gate arrays signal reconstruction three-dimensional sound field genetic algorithms direct coupling hardware prototype acoustic field audio coding surround sound reconstruction

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Decoding ambisonic signals to irregular quad loudspeaker configuration based on hybrid ANN and modified tabu search

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NEURAL COMPUTING & APPLICATIONS 2011年第7期20卷 983-991页

作者： Tsang, P. W. M. Cheung, K. W. K. Leung, A. C. S. City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

Past research has proven that a first-order B-format ambisonic signal can be used to partially reconstruct the original sound field through a collection of arbitrary positioned loudspeakers. This is achieved by setting the gain of each loudspeaker to be the weighted sum of the three components in the B-format signal. Deduction of the weighting factors (a.k.a. the decoding parameters) has been successfully accomplished with the use of the Modified Tabu Search (MTS), and later with the heuristic genetic algorithm (HGA) which provides higher precision and stability. Despite the favorable outcome, both methods involve large amount of iterations and the computation time is lengthy. In this paper, we propose a scheme to overcome this problem based on the integration of Neural Network Estimation (NNE) and the MTS. Compared to HGA, the new approach is about two orders of magnitude faster, and at the same time capable of attaining similar precision in determining the decoding parameters.

关键词： Ambisonic Artificial neural network Modified tabu search heuristic genetic algorithm

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A Data Placement Strategy Based on genetic algorithm for Scientific Workflows

A Data Placement Strategy Based on Genetic Algorithm for Sci...

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8th International Conference on Computational Intelligence and Security (CIS)

作者： Zhao Er-Dun Qi Yong-Qiang Xiang Xing-Xing Chen Yi Cent China Normal Univ Sch Comp Wuhan Peoples R China

ISBN: (纸本)9780769548968

The data placement strategy is an important issue in the scientific workflows which is devoted to reducing the data movements while placing datasets in a few data centers according to the data centers' storage capacity and the data dependency. The data placement is proved to be a NP hard problem, and several methods for this problem like K-means clustering algorithm are presented in the literatures. K-means clustering algorithm can reduce the number of data movements very well, but it may result that the datasets will be concentrated to few data centers, and so the loads of data centers greatly deviate from each other. The paper proposes a data placement strategy based on heuristic genetic algorithm to reduce data movements among the data centers while balancing the loads of data centers. The simulation results show that the proposed algorithm can effectively reduce data movements and balance the load of data centers.

关键词： data placement heuristic genetic algorithm load balancing data dependency

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Solving a Multi-Level Capacitated Lot Sizing Problem with Random Demand via a Fix-and-Optimize heuristic

Solving a Multi-Level Capacitated Lot Sizing Problem with Ra...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Li, Liuxi Song, Shiji Wu, Cheng Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781479974924

In supply chain management, the multi-level multi-product capacitated lot sizing problem (MLCLSP) plays a critical role in operational production systems, where limited resources and time phases are considered. In this paper, we present a stochastic version of MLCLSP subject to capacity constraints. In order to minimize the total expected cost of MLCLSP, a production schedule has to be determined for random demand and a new backlog-oriented delta-service-level measure should be met. We apply scenario method to approximate the stochastic MLCLSP and lead to a new MLCLSP-SCN model. For the purpose of determining production schedule efficiently, robustly and stably, a new heuristic called Fix-and-Optimize heuristic is presented handling random demand. A numerical analysis based on a set of artificial test instances is used to evaluate the relative performance of the heuristic. We further present the numerical results of dynamic programming approach and heuristic genetic algorithm as benchmarks. All the numerical study suggests that our heuristic provides high-quality solutions and the computational effort is moderate.

关键词： Lot sizing random demand Fix-and-Optimize heuristic service level dynamic programming heuristic genetic algorithm

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Research on the Optimization of Multi‐calling Port Ro‐ro Ship Stowage Considering OTD Factors

Frontiers in Economics and Management

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Frontiers in Economics and Management 2021年第11期2卷 32-45页

作者： Hui Zhao

In order to rationally allocate the shipping resources of the shipping company, the factor of OTD for Ro‐ro ship loading orders is introduced, and the order of loading vehicles is based on the principle of ＂first come, first loaded＂ and OTD priority to establish the total sailing cost of Ro‐ro ships with multiple calling ports. In order to minimize the target model, a heuristic algorithm is designed to solve the construction model, and the results of numerical experiments and simulations are used to compare the effects of Lingo and the heuristic algorithm, so as to obtain a more reasonable Ro‐ro ship loading order stowage plan , And prove the effectiveness and robustness of the algorithm.

关键词： Multiple Calling Ports Ro‐ro ship OTD heuristic genetic algorithm

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