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4th Conference on Data Mining and Optimization (DMO)

作者： Khalid, Mohd Nor Akmal Yusof, Umi Kalsom Sabudin, Maziani Univ Sains Malaysia Sch Comp Sci Usm 11800 Penang Malaysia

ISBN: (纸本)9781467327183

Flexible manufacturing system is one of the industrial branches that highly competitive and rapidly expand. Globalization of the industrial system has encouraged the development of distributed manufacturing, including flexible manufacturing system. As such, the complexity of the problem faced in this new environment promotes current researcher to develop various approaches in optimizing the production scheduling. Approaches such as petri net, ant colony, genetic algorithm, intelligent agents, particle swarm optimization, and tabu search are used to apprehend optimization issues. In reality, maintenance is one of the core parts which is important to the manufacturing scheduling as it will affect greatly toward the manufacturing scheduling when the machine breakdown happen. Unfortunately, most approaches disregard the preventive maintenance in the production scheduling problem. In this paper, a harmony search algorithm is introduced to address the problem which includes maintenance. The problem description is successfully represented and the algorithm performance is studied with several parameter tunings.

关键词： distributed-manufacturing flexible manufacturing system harmony search algorithm preventive maintenance

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An Improved Adaptive harmony search algorithm

An Improved Adaptive Harmony Search Algorithm

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10th World Congress on Intelligent Control and Automation (WCICA)

作者： Kong, Zhi Wang, Lifu Wu, Zhaoxia Qi, Shiqing Zou, Dexuan Northeastern Univ Dept Automat & Engn Qinhuangdao 066004 Hebei Peoples R China Xuzhou Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China

ISBN: (纸本)9781467313988

This paper is concerned with the global adaptive harmony search (GAHS) algorithms for solving optimization problems. GAHS employs global information to the adaptive harmony search algorithm. The proposed GAHS algorithm is tested numerically and contrasted with improved harmony search (AHS) algorithm, and particle swarm optimization (PSO). Our simulation results reveal that GAHS is superior to AHS and PSO in terms of robustness and efficiency.

关键词： harmony search algorithm Optimization Adaptive algorithmn Particle swarm optimaizaiton

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A Spectral Clustering-Based Adaptive Hybrid Multi-Objective harmony search algorithm for Community Detection

A Spectral Clustering-Based Adaptive Hybrid Multi-Objective ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Li, Yangyang Chen, Jing Liu, Ruochen Wu, Jianshe Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

ISBN: (纸本)9781467315098

A number of studies has focused on the community detection in complex networks in recent years. Single-objective approaches which have only one optimization function (e. g., modularity or modularity density) may have weaknesses such as just a single community structure can be obtained or resolution limit. In this paper, a spectral clustering-based adaptive hybrid multi-objective harmony search algorithm (SCAH-MOHSA) combined with a local search strategy is proposed to detect the community structure in complex networks. At first, an improved spectral method is employed to convert the community detection problem into a data clustering issue while the length of the representation of a harmony in the harmony memory can be determined. Then, an adaptive hybrid multi-objective harmony search algorithm is used to solve the multi-objective optimization problem so as to resolve the community structure. The experiments on both synthetic and real world networks demonstrate our method achieves partition results which fit the real situation in an even better fashion.

关键词： multi-objective optimization harmony search algorithm complex networks community detection hybrid spectral method

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Traffic Light Scheduling for Pedestrian-Vehicle Mixed-Flow Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第4期20卷 1468-1483页

作者： Zhang, Yi Gao, Kaizhou Zhang, Yicheng Su, Rong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper presents a macroscopic model for pedestrian-vehicle mixed-flow network and a traffic signal scheduling strategy for both pedestrians and vehicles. We first propose a novel mathematical model of pedestrians crossing a junction. By combining a link-based vehicle network model, a traffic light scheduling problem is formulated with the aim to strike a good balance between pedestrians' needs and vehicle drivers' needs. The problem is first converted into a mixed-integer linear programming (MILP) problem via a novel transformation procedure, which is solvable by several existing solvers, e.g., GUROBI. Then a meta-heuristic method called discrete harmony search (DHS) algorithm is also adopted to reduce the computational complexity in MILP. Numerical simulation results are provided to illustrate the effectiveness of our real-time traffic light scheduling strategy for pedestrians and vehicles, and the potential impact of the pedestrian movement to the vehicle traffic flows.

关键词： Urban traffic signal scheduling macroscopic pedestrian flow model macroscopic vehicle flow model mixed logical constraints mixed integer linear programming harmony search algorithm

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A Modified harmony search algorithm for Optimization Problems

A Modified Harmony Search Algorithm for Optimization Problem...

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5th International Symposium on Computational Intelligence and Design (ISCID)

作者： Yang, Jiali Zhu, Jihong Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9780769548111

harmony search (HS) algorithm is a new meta-heuristic optimization algorithm inspired from the music improvisation process. Though the excellent performance makes it wildly used in many areas, it is easy to get trapped in local minima. This paper is to introduce a modified harmony search algorithm to improve the performance of HS algorithm. The method is implemented by adding an "innovative" process on global harmony search algorithm (GHSA), a variant of HS algorithm. This modification would be easy to make a balance of diversification and intensification. The benchmarks are designed to test the performance of the modified HS algorithm and the results show that the innovative HS algorithm has a better performance than the basic HS algorithm and its variants. In addition, to investigate the influence of parameter bw on the algorithm's performance, the tests are also conducted under the parameter bw with different types.

关键词： harmony search algorithm Evolutionary algorithms Global optimization

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An Amended harmony search algorithm for Unconstrained Optimization Problems

An Amended Harmony Search Algorithm for Unconstrained Optimi...

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3rd International Conference on Digital Manufacturing and Automation (ICDMA 2012)

作者： Li Ruoping Ouyang Haibin Gao Liqun Northeastern Univ Informat & Sci Coll Shenyang 110004 Peoples R China

ISBN: (纸本)9783037854624

harmony search (HS) algorithm is a new mate heuristic algorithm, which is conceptualized using the musical improvisation process of searching for a perfect state of harmony. Its own potential and shortage, one of its main disadvantages is that it easily trapped into local optima and converges very slowly. Based on the conception of swarm intelligence, this paper presents an amended harmony search (AHS) algorithm. AHS introduces a novel position updating strategy for generating new solution vectors, which enhances solution accuracy and convergence rate of algorithm. Several standard benchmark optimization functions are to be test and compare the performance of the AHS. The results revealed the superiority of the proposed method to the HS and its three improved algorithms (IHS, OHS and NGHS).

关键词： harmony search algorithm local optima swarm intelligence amended harmony search algorithm position updating

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Feature Selection Using harmony search for Script Identification from Handwritten Document Images

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JOURNAL OF INTELLIGENT SYSTEMS 2018年第3期27卷 465-488页

作者： Singh, Pawan Kumar Das, Supratim Sarkar, Ram Nasipuri, Mita Jadavpur Univ Dept Comp Sci & Engn 188 Raja SC Mullick Rd Kolkata 700032 W Bengal India

The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the irrelevant, noisy, and non-contributing features, resulting in acceptable classification accuracy. harmony search algorithm (HSA) is an evolutionary algorithm that is applied to various optimization problems such as scheduling, text summarization, water distribution networks, vehicle routing, etc. This paper presents a hybrid approach based on support vector machine and HSA for wrapper feature subset selection. This approach is used to select an optimized set of features from an initial set of features obtained by applying Modified log-Gabor filters on prepartitioned rectangular blocks of handwritten document images written in either of 12 official Indic scripts. The assessment justifies the need of feature selection for handwritten script identification where local and global features are computed without knowing the exact importance of features. The proposed approach is also compared with four well-known evolutionary algorithms, namely genetic algorithm, particle swarm optimization, tabu search, ant colony optimization, and two statistical feature dimensionality reduction techniques, namely greedy attribute search and principal component analysis. The acquired results show that the optimal set of features selected using HSA gives better accuracy in handwritten script recognition.

关键词： harmony search algorithm wrapper feature selection handwritten script identification Indic scripts Modified log-Gabor filters support vector machine

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Jointly rostering, routing, and rerostering for home health care services: A harmony search approach with genetic, saturation, inheritance, and immigrant schemes

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 115卷 151-166页

作者： Lin, Chun-Cheng Hung, Lun-Ping Liu, Wan-Yu Tsai, Ming-Chun Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan Natl Taipei Univ Nursing & Hlth Sci Dept Informat Management Taipei 108 Taiwan Natl Chung Hsing Univ Dept Forestry Taichung 402 Taiwan

In home health care (HHC) services, nurses or professional caregivers are dispatched to patients' homes to provide medical care services, such that each patient can stay at home to be treated periodically. The HHC problem consists of the nurse rostering problem (NRP) and the vehicle routing problem with time windows (VRPTW), both of which are NP-hard problems, which are harder or equal to the hardest problem in the NP (nondeterministic polynomial time) problem class and generally cannot be solved efficiently. To the best of our knowledge, the previous algorithmic approaches were designed to separately address NRP and VRPTW of the HHC problem. However, NRP and VRPTW of the HHC problem are intercorrelated, and their respective optimal objectives may be in conflict with each other in many cases. Additionally, the problem generally involves too many constraints to be solved, and most previous works did not address occurrence of sudden incidents in HHC services (e.g., a nurse or a patient suddenly requests for a leave, and a patient suddenly changes the time slot to be treated) such that the original nurse roster could become infeasible. Under constraints of nurse qualifications, working laws, nurse preferences, and vehicle routing, the first model considers nurse rostering and vehicle routing concurrently to minimize total costs of nurse overtime and vehicle routing. The second model extends the first model with rerostering caused by occurrence of sudden incidents. harmony search algorithm (HSA) has been shown to perform better in solving NRPs than conventional metaheuristics, and hence this work proposes an improved HSA with genetic and saturation schemes, in which the solution representation is designed to concurrently determine nurse rostering and vehicle routing. For the second rerostering model, inheritance and immigrant schemes are added to the HSA to adapt to the change caused by occurrence of sudden incidents. Experimental results show that the proposed HSA perf

关键词： Home health care service Nurse rostering Vehicle routing problem Rerostering harmony search algorithm Metaheuristic algorithm

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Clustering and Classification based real time analysis of health monitoring and risk assessment in Wireless Body Sensor Networks

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BIO-algorithmS AND MED-SYSTEMS 2019年第4期15卷

作者： Alameen, Abdalla Gupta, Ashu Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Coll Arts & Sci Wadi Al Dawasir Al Kharj Saudi Arabia

Wireless body sensor networks (WBSNs) play a vital role in monitoring the health conditions of patients and are a low-cost solution for dealing with several healthcare applications. However, processing a large amount of data and making feasible decisions in emergency cases are the major challenges attributed to WBSNs. Thus, this paper addresses these challenges by designing a deep learning approach for health risk assessment by proposing fractional cat based salp swarm algorithm (FCSSA). At first, the WBSN nodes are utilized for sensing data from patient health records to acquire certain parameters for making the assessment. Based on the obtained parameters, WBSN nodes transmit the data to the target node. Here, the hybrid harmony search algorithm and particle swarm optimization (hybrid HSA-PSO) is used for determining the optimal cluster head. Then, the results produced by the hybrid HSA-PSO are given to the target node, in which the deep belief network (DBN) is used for classifying the health records for the health risk assessment. Here, the DBN is trained using the proposed FCSSA, which is developed by integrating fractional cat swarm optimization (FCSO) and salp swarm algorithm (SSA) for initiating the classification. The proposed FCSSA-based DBN shows better performance using metrics, namely accuracy, energy, and throughput with values 94.604, 0.145, and 0.058, respectively.

关键词： classification deep belief network fractional cat swarm optimization harmony search algorithm health risk assessment particle swarm optimization wireless body sensor network

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Relevant feature selection in the context of painting classification

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PATTERN ANALYSIS AND APPLICATIONS 2019年第4期22卷 1455-1468页

作者： Huang, Yin-Fu Wang, Chang-Tai Hsieh, Yun-Shin Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn 123 Univ RdSect 3 Touliu 640 Yunlin Taiwan

In this paper, a painting genre classification system is proposed. Four feature descriptors about the color and texture defined in the MPEG-7 specification, which are more against painting characteristics, are extracted from data sets. Then, we use a self-adaptive harmony search algorithm to select relevant features (or a local feature set) to train each one-against-one SVM classifier. Finally, a majority voting strategy on N(N - 1)/2 prediction results would determine their respective genres of paintings. The experimental results show that the overall accuracy reaches 69.8%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results. Besides, we also verify that if the number of painting genres is few, the classification method without feature selection will be sufficient to obtain good results. Finally, we propose new measurement to assess the methods with different datasets.

关键词： Painting genre classification MPEG-7 specification harmony search algorithm Majority voting strategy

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