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Application of Improved electromagnetism-like mechanism algorithm on Massive Remote Sensing Image Screening

APPLIED SCIENCES-BASEL 2025年第7期15卷 3542-3542页

作者： Fang, Yingjie Liu, Kun Zhang, Xun Lin, Gang Wang, Huibing Dong, Lina Li, Su Beijing Technol & Business Univ Coll Comp & Artificial Intelligence Beijing 100048 Peoples R China Minist Nat Resources Peoples Republ China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Minist Nat Resources Key Lab Coupling Proc & Effects Nat Resource Eleme Beijing 100055 Peoples R China China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

In recent years, remote sensing data have gradually shown a trend towards big data. The cost of the efficient storage, transmission, and processing of multi-source massive remote sensing data is very high. In practical applications, it is usually necessary to screen out data that meet specific coverage requirements from a large amount of remote sensing data, which can be regarded as a set covering problem. The traditional manual screening method is time-consuming and labor-intensive, making it difficult to meet the needs of large-scale information processing and analysis tasks across multiple fields. To improve the screening efficiency, people usually adopt the greedy algorithm for data screening, which may lead to becoming trapped in a local optimal solution. In this paper, an improved electromagnetism-like mechanism algorithm is proposed to solve the optimal screening problem of remote sensing data, using the tent chaotic map to construct the initial population, combining with a new local search strategy, and introducing the idea of differential evolutionary algorithm. The experimental results show that the improved algorithm has significant advantages in the optimal screening problem of massive remote sensing data. Compared with the greedy algorithm, the optimal solution of the improved algorithm is increased by about 9.78% on average. Compared with the original algorithm, it can search for the global optimal solution more quickly and has better robustness.

关键词： remote sensing image optimization set covering problem electromagnetism-like mechanism algorithm tent chaotic map differential evolution algorithm

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An improved electromagnetism-like mechanism algorithm for energy-aware many-objective flexible job shop scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第7-8期119卷 4265-4275页

作者： Qu, Minghao Zuo, Ying Xiang, Feng Tao, Fei Beihang Univ Sino French Engineer Sch Beijing 100191 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Nowadays, most of the manufacturing industries rely on effective shop floor schedules to improve productivity and to optimize the makespan, production cost, tardiness, etc., which are usually considered in traditional scheduling problems. Recently, in response to the global initiatives for sustainability in manufacturing industries, an increasing number of shop floor schedules have taken energy consumption into account. However, few research works have considered traditional objectives, energy consumption, and other sustainability factors simultaneously in a shop floor schedule. In this paper, a many-objective optimization model for a flexible job shop scheduling problem considering makespan, total energy consumption, and three other indicators is formulated. Then, an improved electromagnetism-like mechanism algorithm is proposed to find the optimal or near-optimal solutions. Finally, a real-life case study is conducted to evaluate the proposed model and the algorithm. The results show that the many-objective model is effective for reducing energy consumption and improving sustainability in the shop floor.

关键词： electromagnetism-like mechanism algorithm Energy consumption Job shop scheduling Many-objective optimization Sustainable manufacturing

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An electromagnetism-like mechanism algorithm for the router node placement in wireless mesh networks

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SOFT COMPUTING 2019年第12期23卷 4407-4419页

作者： Sayad, Lamri Bouallouche-Medjkoune, Louiza Aissani, Djamil Univ Bejaia Fac Exact Sci LAMOS Lab Bejaia 06000 Algeria Msila Univ Dept Comp Sci Msila 28000 Algeria

In this paper, we consider the problem of mesh router placement in a wireless mesh network (WMN). The latter is an emerging networking technology consisting of three kinds of nodes: mesh clients, mesh routers and gateways. Mesh routers form a backbone to forward data between client nodes and the external network. Therefore, the optimization of mesh routers positions strongly influences the performance of the WMN. Since this issue has already been proved as being computationally NP-hard to solve, the use of non-exact methods (such as heuristics and metaheuristics) is indispensable. In this sense, our current work consists to apply and adapt the electromagnetism-like mechanism (EM) metaheuristic to solve the router node placement issue. The idea is to consider a population of solutions encoded as particles subject to attractions and repulsions as in electromagnetic systems. Finally, we have evaluated our proposed approach by simulating different scenarios under various settings. The obtained results indicate that the proposed EM algorithm outperforms the existing particle swarm intelligence algorithm and genetic algorithm in defining near optimal positions for mesh routers with regard to coverage and connectivity.

关键词： Wireless mesh network Router node placement Facility location problem Network design electromagnetism-like mechanism algorithm

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Bi-Objective Optimization of Service-Oriented Location-Pricing Model Using electromagnetism-like mechanism algorithm

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2020年第6期19卷 1737-1769页

作者： Alinezhad, Alireza Hajipour, Vahid Hosseinzadeh, Sanaz Islamic Azad Univ Qazvin Branch Fac Ind & Mech Engn Qazvin Iran Islamic Azad Univ Coll Engn West Tehran Branch Dept Ind Engn Tehran Iran

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM.

关键词： Location-pricing problem service facilities congested systems multi-objective optimization electromagnetism-like mechanism algorithm

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A modified electromagnetism-like mechanism algorithm with pattern search for global optimisation

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2018年第4期16卷 430-440页

作者： Wu, Qing Zhang, Chunjiang Gao, Liang Huazhong Agr Univ Coll Engn Wuhan 430070 Hubei Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

The solution space of most global optimisation problems is very complex, which puts forward the high requirement for the search performance of algorithms. electromagnetism-like mechanism (EM) algorithm is a rising global optimisation method. However, the intensification and the diversification of the original EM are not very efficient. This paper proposes a modified EM algorithm. To improve the intensification ability, a more effective variable step size pattern search has been applied to replace the original random line search at the local search stage. Meanwhile, a perturbing point is used to increase the diversity. In addition, the formula of calculating the total force is simplified to accelerate the algorithm's searching process. Numerical experiments are conducted to compare the proposed algorithm with other variants of EM algorithms and different variants of particle swarm optimisation algorithms. The results show that the approach is competitive.

关键词： electromagnetism-like mechanism algorithm pattern search global optimisation meta-heuristic algorithm local search

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Applications of An Improved electromagnetism-like mechanism algorithm in Optimization of Processing Parameters 2018

Applications of An Improved Electromagnetism-like Mechanism ...

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6th International Conference on Information Technology - IoT and Smart City (ICIT)

作者： Wu, Qing Xu, Weigang Huang, Weijun Huazhong Agr Univ Coll Engn Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9781450366298

The selection of processing parameters has great influence on the work-piece quality, processing time and processing costs. However the optimization of processing parameters is a complex nonlinear constraint optimization problem which is difficult to solve by traditional methods. In order to solve this problem effectively, this paper proposes a kind of improved electromagnetism-like mechanism (EM) algorithm. First, simplify the calculation process of the power and force and improve the power and force calculation formula to speed up the convergence speed and to enhance the search ability of EM algorithm. Then improve the particle movement formula by introducing movement probability. Meanwhile the Solis & Wets search algorithm is combined with the EM algorithm to improve the local search ability. For constrained optimization problems, a processing constraints method is developed by introducing a kind of feasibility and dominance rules into the EM algorithm. This improved EM algorithm is applied in the grinding parameters optimization problem and abrasive water jet machining parameter optimization problem. The results show that the proposed method is better than other algorithms and achieves significant improvement, which verifies the feasibility and superiority of this new algorithm.

关键词： electromagnetism-like mechanism algorithm Constraint optimization Optimization of grinding processing parameters Optimization of abrasive water jet processing parameters

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Analysis of the Effect of Search Step Size on the Accuracy and Convergence Properties of electromagnetism-like mechanism algorithm

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2017年第4-5期28卷 429-441页

作者： Tan, Jian Ding Dahari, Mahidzal Koh, Siaw Paw Koay, Ying Ying Abed, Issa Ahmed Univ Malaya Dept Mech Engn Kuala Lumpur 50603 Malaysia Univ Tenaga Nas Coll Engn Kajang 43000 Selangor Malaysia Southern Tech Univ Engn Tech Coll Basrah Basra Iraq

An electromagnetism-like mechanism algorithm (EM) is a meta heuristic search algorithm known for its ability to solve complex global optimization problems. In a standard EM, the size of the search steps is randomly determined. In this paper, an investigation is carried out to analyze the effects of search step size on the overall convergence performance of EM. A standard EM is modified into two different sets of algorithms with different extremes of search step sizes. The modified algorithms are then tested in 10 standard benchmark functions to evaluate the effect of search step sizes on the performance of each. The results show that the modified EM with smaller search steps performed a much more detailed search for possible solutions in all dimensions. This enabled the overall convergence to achieve relatively higher accuracy in objective values.

关键词： electromagnetism-like mechanism algorithm global optimization meta-heuristic search step size

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A Practical electromagnetism-like mechanism algorithm

A Practical Electromagnetism-like Mechanism Algorithm

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IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference

作者： Shuang-ji Wang Xiao-lei Cui No.91388 Troops of PLA Zhanjiang China

According to the problems existing in the original electromagnetism-like mechanism (EM) algorithm, such as heavy computation, low convergence rate, and pathological feature in resultant force formula, this paper proposes a new practical EM algorithm after analyzing the function optimization principle of the original algorithm. By using normalizing the objective function value, the power formula is simplified. By introducing the correction factor into resultant force formula, the pathological feature in the resultant force formula is eliminated. Meanwhile the impact of distance factor on the force between particles in population is weakened, and the convergence rate is increased. Experiments show that the improved algorithm has a better convergence result and higher efficiency.

关键词： electromagnetism-like mechanism algorithm Function optimization Normalization Correction factor

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An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus

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JOURNAL OF BIOMEDICAL INFORMATICS 2015年 54卷 220-229页

作者： Wang, Kung-Jeng Adrian, Angelia Melani Chen, Kun-Huang Wang, Kung-Min Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Shin Kong Wu Ho Su Mem Hosp Dept Surg Taipei 111 Taiwan

Recently, the use of artificial intelligence based data mining techniques for massive medical data classification and diagnosis has gained its popularity, whereas the effectiveness and efficiency by feature selection is worthy to further investigate. In this paper, we presents a novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetism-like mechanism (IEM) algorithm. Nearest neighbor algorithm is served as a classifier for the wrapper method. The proposed IEM algorithm is compared with nine popular feature selection and classification methods. Forty-six datasets from the UCI repository and eight gene expression microarray datasets are collected for comprehensive evaluation. Non-parametric statistical tests are conducted to justify the performance of the methods in terms of classification accuracy and Kappa index. The results confirm that the proposed IEM method is superior to the common state-of-art methods. Furthermore, we apply IEM to predict the occurrence of Type 2 diabetes mellitus (DM) after a gestational DM. Our research helps identify the risk factors for this disease;accordingly accurate diagnosis and prognosis can be achieved to reduce the morbidity and mortality rate caused by DM. (C) 2015 Elsevier Inc. All rights reserved.

关键词： electromagnetism-like mechanism algorithm Nearest-neighbor heuristic Opposite sign test Feature selection Diabetes mellitus

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Research of Big Data Analysis on Rough Set and electromagnetism-like mechanism algorithm 14

Research of Big Data Analysis on Rough Set and Electromagnet...

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IEEE International Conference on Computer and Information Technology (CIT 2014)

作者： Yun, Shang Lanzhou Vocat Tech Coll Informat Engn Dept Lanzhou Gansu Peoples R China

ISBN: (纸本)9781479962396

The data volume and the complexity of query and analysis are the main features of big data analysis. Rough set theory is led in to enhance information processing capacity, electromagnetism-like mechanism algorithm can also avoid local optimum search, big data analysis system based on three-tier structure is built to deal with noise processing deficiencies and uncertain factors on data collection in previous analysis techniques. Some challenging research problems in the future are discussed.

关键词： big data electromagnetism-like mechanism algorithm rough set reduction

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