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International Conference on Networks Security, Wireless Communications and Trusted Computing

作者： Zhen, Tong Zhang, Qiuwen Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9780769536101

The multi-depot vehicle routing problem with time windows (MDVRPTW) is an extension to the classical Vehicle Routing Problem (VRP);it is the major research topics in the supply chain management field. It aims to designing a set of minimum-cost routes for a vehicle fleet servicing many customers with known demands and predefined time windows. In this paper a hybrid metaheuristic algorithm is proposed to solve MDVSPTW successfully by ants transfer policy and algorithm to construct solution designed in this dissertation. To improve hybrid ant colony algorithm performance, a local search improvement algorithm that explores a large neighborhood of the current solution to discover a cheaper set of feasible routes. The neighborhood structure comprises all solutions that can be generated by iteratively performing node exchanges among nearby trips followed by a node reordering on every route. The experiment indicates the validity of the technique to MDVSPTW with the above-mentioned conditions.

关键词： hybrid metaheuristic algorithm multi-depot vehicle routing problem with time windows

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A Hybrid metaheuristic algorithm for the Logistics Routing-Loading Scheduling Problem

A Hybrid Metaheuristic Algorithm for the Logistics Routing-L...

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International Conference on MultiMedia and Information Technology

作者： Zhen, Tong Zhang, Qiuwen Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9780769535562

In this paper a Logistics transportation problem including routing, scheduling and loading tasks is presented. Most of the related works only involve the solution of routing and scheduling, as a combination of up to six different types of VRPs (Rich VRP), leaving away the loading task, which are not enough to define more complex real-world cases. We propose a solution methodology for transportation instances that involve six types of VRPs, a new constraint that limits the number of vehicles that can be attended simultaneously and the loading tasks. They are solved using an Ant Colony System algorithm, which is a hybrid metaheuristic. Results from a computational test using real-world instances show that the proposed approach outperforms the transportation planning related to manual designs. Besides a well-known VRP benchmark was solved to validate the approach.

关键词： hybrid metaheuristic algorithm Logistics Routing-Loading Scheduling Problem

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An Effective metaheuristic algorithm for Intrusion Detection System

An Effective Metaheuristic Algorithm for Intrusion Detection...

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2nd IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Chen, Ze-Hong Tsai, Chun-Wei Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan

ISBN: (纸本)9781538685426

Intrusion detection system (IDS) is typically used to detect and prevent abnormal behaviors in a network management system. The basic idea of IDS is to use feature values from network packet capture mechanism to classify whether a behavior is abnormal. However, most traditional classification algorithms are incapable of recognizing unknown behaviors. To develop a high performance classification algorithm to improve the accuracy of IDS, the algorithm proposed in this paper will integrate clustering, classification, and metaheuristic algorithms as a classification algorithm for IDS, called search economics with k-means and support vector machine (SEKS). Moreover, this hybrid strategy for the proposed algorithm is aimed at improving the accuracy of abnormal behavior detection of such a system, reducing the computation time of a classification algorithm, and making it possible for the IDS to recognize the unknown and new variant attacks in a network environment. The experimental results show that the proposed algorithm outperforms all the other classification algorithms compared in this paper in terms of the accuracy.

关键词： metaheuristic algorithm intrusion detection system and classification algorithm

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Electrochemical Model Parameter Estimation of a Lithium-Ion Battery Using a metaheuristic algorithm : Cascaded Improved Harmony Search

Electrochemical Model Parameter Estimation of a Lithium-Ion ...

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10th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Chun, Huiyong Han, Soohee Pohang Univ Sci & Technol POSTECH Dept Creat IT Engn Pohang 37673 South Korea

For the safe and accurate operation of lithium-ion battery, the estimation of inner states of battery is necessary. Among many models of battery, electrochemical model, especially P2D model, represents the real dynamics such as transport and diffusion phenomena of charges and lithium-ions. For this reason, P2D model is an appropriate model for estimating the states. In this paper, metaheuristic algorithm is applied to estimate these parameter values, since it is more useful to implement the metaheuristic method than other jacobian-based methods, if the number of the parameters to be estimated is increased. Harmony search is one of the metaheuristic algorithms, and improved harmony search was developed for high convergence rate and fine-tunning. The cascaded version of improved harmony search is proposed as the estimation algorithm in this paper, and it is the first time to using the harmony search for lithium-ion battery parameterization. As a result, the parameters had reasonable values compared to reference values and lower voltage, temperature errors compared to the results of improved harmony search which is employed as a benchmark. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Lithium-ion battery parameter estimation metaheuristic algorithm Cascaded improved harmony search

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Improved metaheuristic algorithm Based Finite Element Model Updating of a Hybrid Girder Cable-Stayed Railway Bridge

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BUILDINGS 2022年第7期12卷 958-958页

作者： Qin, Shiqiang Yuan, Yonggang Gan, Yaowei Wang, Qiuping Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Peoples R China

This study proposes a generally applicable improvement strategy for metaheuristic algorithms, improving the algorithm's accuracy and local convergence in finite element (FE) model updating. Based on the idea of "survival of the fittest" in biological evolution, the improvement strategy introduces random crossover and mutation operators into metaheuristic algorithms to improve the accuracy and stability of the solution. The effectiveness of the improvement strategy with three typical metaheuristic algorithms was comprehensively tested by benchmark functions and numerical simulations of a space truss structure. Meanwhile, the initial FE model of a railway hybrid girder cable-stayed bridge was updated to examine the effect of the improved metaheuristic algorithm within the FE model, updating for complex engineering structures. The results show that the accuracy and stability of the improved metaheuristic algorithm were improved by this process. After the initial FE model of the hybrid girder cable-stayed bridge was updated, the calculated frequencies and displacements were closer to the measured values, better representing the actual structure, and showing that this process can be used for baseline FE models of bridges.

关键词： metaheuristic algorithm model updating random crossover hybrid girder cable-stayed bridge kriging model

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Flood algorithm: a novel metaheuristic algorithm for optimization problems

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2278-e2278页

作者： Ozkan, Ramazan Samli, Ruya Natl Def Univ Dept Comp Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Dept Comp Engn Istanbul Turkiye

metaheuristic algorithms are an important area of research that provides significant advances in solving complex optimization problems within acceptable time periods. Since the performances of these algorithms vary for different types of problems, many studies have been and need to be done to propose different metaheuristic algorithms. In this article, a new metaheuristic algorithm called flood algorithm (FA) is proposed for optimization problems. It is inspired by the flow of flood water on the earth's surface. The proposed algorithm is tested both on benchmark functions and on a real-world problem of preparing an exam seating plan, and the results are compared with different metaheuristic algorithms. The comparison results show that the proposed algorithm has competitive performance with other metaheuristic algorithms used in the comparison in terms of solution accuracy and time.

关键词： metaheuristic algorithm Flood algorithm Optimization Exam seat planning

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Optimized Design of the Lithium Niobate for Spectrally-Pure-State Generation at MIR Wavelengths Using metaheuristic algorithm

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ADVANCED QUANTUM TECHNOLOGIES 2022年第7期5卷

作者： Cai, Wu-Hao Tian, Ying Wang, Shun You, Chenglong Zhou, Qiang Jin, Rui-Bo Wuhan Inst Technol Hubei Key Lab Opt Informat & Pattern Recognit Wuhan 430205 Peoples R China Louisiana State Univ Dept Phys & Astron Quantum Photon Lab Baton Rouge LA 70803 USA Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn Chengdu 610054 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Quantum Sci & Engn Shenzhen 518055 Peoples R China

Quantum light sources in the mid-infrared (MIR) band play an important role in many applications, such as quantum sensing, quantum imaging, and quantum communication. However, there is still a lack of high-quality quantum light sources in the MIR band, such as the spectrally pure single-photon source. In this work, the generation of a spectrally-pure state in an optimized poled lithium niobate crystal using a metaheuristic algorithm is presented. In particular, the particle swarm optimization algorithm is adopted to optimize the duty cycle of the poling period of the lithium niobate crystal. With this approach, the spectral purity can be improved from 0.820 to 0.998 under the third group-velocity-matched condition, and the wavelength-tunable range from 3.0 to 4.0 mu m for the degenerate case and 3.0 to 3.7 mu m for the nondegenerate case. This work paves the way for developing quantum photonic technologies at the MIR wavelength band.

关键词： lithium niobate crystals metaheuristic algorithm mid-infrared quantum light sources

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Particle guided metaheuristic algorithm for global optimization and feature selection problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Kwakye, Benjamin Danso Li, Yongjun Mohamed, Halima Habuba Baidoo, Evans Asenso, Theophilus Quachie Univ Sci & Technol China Sch Management Hefei 230026 Anhui Peoples R China Tech Univ Carolo Wilhelmina Braunschweig Inst High Frequency Tech Univ Pl 2 D-38106 Braunschweig Germany Univ Oslo Fac Med Domus Med Postbokx1122 Blindern N-0317 Oslo Norway

Optimization problems can be seen in numerous fields of practical studies. One area making waves in the application of optimization methods is data mining in machine learning. An important preprocessing technique of data mining where irrelevant variables are discarded from the datasets and holding onto variables with important information is referred to as feature selection (FS). FS is critical to tackling the 'curse of dimensionality' by reducing the number of features, minimizing computational expensiveness and maximizing the accuracy of the machine learning models. Swarm Intelligence (SI)-based meta-heuristic algorithms (MAs) have been widely employed to solve several optimization problems like FS. However, common drawbacks identified with these algorithms include getting trapped in local optima, especially in situations where the search space is large (high dimensional space). This study proposes a new hybrid SI-based MA called Particle Swarm-guided Bald Eagle Search (PS-BES). The algorithm utilizes the speed of Particle Swarm to guide Bald Eagles in their search to ensure a smooth transition of the algorithm from exploration to exploitation. Additionally, we introduce the Attack-Retreat-Surrender technique, a new local-optima escape technique to enhance the balance between diversification and intensification of PS-BES. To establish the outstanding performance of the proposed algorithm, PS-BES is comprehensively analyzed utilizing 26 Benchmark functions. Further, the practicality of PS-BES is highlighted by its binary version for feature selection and evaluated using 27 classification datasets from the UCI repository. The results prove the overall superiority of PS-BES and bPS-BES as opposed the 10 state-of-the-art algorithms employed in the study.

关键词： Feature selection Global optimization metaheuristic algorithm Particle swarm optimization Bald eagle search Hybrid algorithm

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Levy Arithmetic algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Barua, Sujoy Merabet, Adel St Marys Univ Div Engn Halifax NS Canada St Marys Univ Div Engn 923 Robie St Halifax NS B3H 3C3 Canada

Levy Arithmetic algorithm is an upgraded metaheuristic optimization approach proposed to enhance the Arithmetic Optimization algorithm using the Levy random step. Arithmetic Optimization algorithm addresses diverse optimization problems by employing arithmetic operators. However, its linear search capability might hinder attaining optimal solutions, which leads to stagnation. In this study, the Levy Arithmetic algorithm is introduced by combining the Arithmetic Optimization algorithm and the Levy random step to enhance search capabilities and minimize computational demands for improved outcomes. The evaluation encompassed ten CEC2019 benchmark functions, four established real-world engineering problems, and the Economic Load Dispatch of microgrids with renewable energy integration. Using evaluation metrics such as standard deviation and mean, along with hypothesis tests, the study conducts a thorough comparison between the performance of the proposed algorithm and that of the conventional Arithmetic Optimization algorithm. The results show that the Levy Arithmetic algorithm achieves optimization with a minimized number of evaluations in terms of standard deviation and mean, compared to the Arithmetic Optimization algorithm. Additionally, the proposed method was compared with various well-known and recent metaheuristics algorithms, and the Levy Arithmetic algorithm consistently demonstrates superior performance, especially in contrast to the Arithmetic Optimization algorithm.

关键词： Arithmetic Optimization algorithm Levy Arithmetic algorithm metaheuristic algorithm CEC Benchmarks Engineering Problems Microgrid ELD

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Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic algorithm for engineering optimization

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KNOWLEDGE-BASED SYSTEMS 2024年 295卷

作者： Wang, Xiaopeng Snasel, Vaclav Mirjalili, Seyedali Pan, Jeng-Shyang Kong, Lingping Shehadeh, Hisham A. VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane 4006 Australia Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Amman Arab Univ Coll Comp Sci & Informat Amman 11953 Jordan

This study proposes a novel artificial protozoa optimizer (APO) that is inspired by protozoa in nature. The APO mimics the survival mechanisms of protozoa by simulating their foraging, dormancy, and reproductive behaviors. The APO was mathematically modeled and implemented to perform the optimization processes of metaheuristic algorithms. The performance of the APO was verified via experimental simulations and compared with 32 state-of-the-art algorithms. Wilcoxon signed-rank test was performed for pairwise comparisons of the proposed APO with the state-of-the-art algorithms, and Friedman test was used for multiple comparisons. First, the APO was tested using 12 functions of the 2022 IEEE Congress on Evolutionary Computation benchmark. Considering practicality, the proposed APO was used to solve five popular engineering design problems in a continuous space with constraints. Moreover, the APO was applied to solve a multilevel image segmentation task in a discrete space with constraints. The experiments confirmed that the APO could provide highly competitive results for optimization problems. The source codes of Artificial Protozoa Optimizer are publicly available at https://***/projects and https://***/matlabcentral/ fileexchange/162656-artificial-protozoa-optimizer.

关键词： metaheuristic algorithm Artificial protozoa optimizer Constrained optimization Engineering design Image segmentation

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