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A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers

APPLIED SOFT COMPUTING 2011年第8期11卷 5270-5280页

作者： Pan, Quan-Ke Wang, Ling Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a chaotic harmony search (CHS) algorithm is proposed to minimize makespan for the permutation flow shop scheduling problem with limited buffers. First of all, to make the harmony search algorithm suitable for solving the problem under consideration, a rank-of-value rule is applied to convert continuous harmony vectors to discrete job permutations. Secondly, an efficient initialization scheme based on the Nawaz-Enscore-Ham heuristic [M. Nawaz, E.E.J. Enscore, I. Ham, A heuristic algorithm for the m-machine, n-job flow shop sequencing problem, OMEGA-International Journal of Management Science 11 (1983) 91-95] and its variants is presented to construct an initial harmony memory with a certain level of quality and diversity. Thirdly, a new improvisation scheme is developed to well inherit good structures from the best harmony vector in the last generation. In addition, a chaotic local search algorithm with probabilistic jumping scheme is presented and embedded in the proposed CHS algorithm to enhance the local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It is shown that the proposed CHS algorithm generates better results not only than the two recently developed harmony search algorithms but also than the existing hybrid genetic algorithm and hybrid particle swarm optimization in terms of solution quality and robustness. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Flow shop with limited buffers Makespan harmony search algorithm Chaotic optimization

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A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints

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APPLIED ENERGY 2016年 183卷 791-804页

作者： Zhang, Jingrui Wu, Yihong Guo, Yiran Wang, Bo Wang, Hengyue Liu, Houde Xiamen Univ Dept Instrumental & Elect Engn Xiamen Peoples R China Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equip Xian Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester Lancs England Tsinghua Univ Shen Zhen Grad Sch Shenzhen Peoples R China

With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR, the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Microgrid Day-ahead scheduling Power flow harmony search algorithm Differential evolution

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An improved differential harmony search algorithm for function optimization problems

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SOFT COMPUTING 2019年第13期23卷 4827-4852页

作者： Wang, Lin Hu, Huanling Liu, Rui Zhou, Xiaojian Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

To overcome the drawbacks of the harmony search (HS) algorithm and further enhance its effectiveness and efficiency, an improved differential HS (IDHS) is proposed to solve numerical function optimization problems. The proposed IDHS has a novel improvisation scheme that integrates DE/best/1/bin and DE/rand/1/bin from the differential evolution (DE) algorithm to enhance its local search and exploration capabilities and a new pitch adjustment rule that benefits from the best solution in the harmony memory to increase its convergence speed. With dynamically adjusted parameters, the proposed IDHS can balance exploitation and exploration throughout the search process. The numerical results of an experiment with classic testing functions and those of a comparative experiment show that IDHS outperforms eight algorithms in the HS family and three widely used population-based algorithms in different families, including DE, particle swarm optimization, and improved fruit fly optimization algorithm. IDHS demonstrates fast convergence and an especially good capability to handle difficult high-dimensional optimization problems.

关键词： harmony search algorithm Differential evolution Meta-heuristics Global optimization

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A novel hybrid algorithm combining hunting search with harmony search algorithm for training neural networks

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JOURNAL OF THE OPERATIONAL REsearch SOCIETY 2013年第5期64卷 748-761页

作者： Kulluk, S. Erciyes Univ TR-38039 Kayseri Turkey

Neural networks (NNs) are one of the most widely used techniques for pattern classification. Owing to the most common back-propagation training algorithm of NN being extremely computationally intensive and it having some drawbacks, such as converging into local minima, many meta-heuristic algorithms have been applied to training of NNs. This paper presents a novel hybrid algorithm which is the integration of harmony search (HS) and Hunting search (HuS) algorithms, called h_HS-HuS, in order to train Feed-Forward Neural Networks (FFNNs) for pattern classification. HS and HuS algorithms are recently proposed meta-heuristic algorithms inspired from the improvisation process of musicians and hunting of animals, respectively. harmony search builds up the main structure of the hybrid algorithm, and HuS forms the pitch adjustment phase of the HS algorithm. The performance proposed algorithm is compared to conventional and meta-heuristic algorithms. Empirical tests are carried out by training NNs on nine widely used classification benchmark problems. The experimental results show that the proposed hybrid harmony-hunting algorithm is highly capable of training NNs. Journal of the Operational Research Society (2013) 64, 748-761. doi:10.1057/jors.2012.79 Published online 22 August 2012

关键词： neural networks supervised training heuristics harmony search algorithm hunting search algorithm

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Training HMM Structure and Parameters with Genetic algorithm and harmony search algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2012年第1期7卷 109-114页

作者： Ko, Kwang-Eun Park, Seung-Min Park, Junheong Sim, Kwee-Bo Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

关键词： Hidden markov model Genetic algorithm harmony search algorithm Optimization

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Maximizing Wireless Sensor Network Coverage With Minimum Cost Using harmony search algorithm

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IEEE SENSORS JOURNAL 2017年第3期17卷 882-896页

作者： Alia, Osama Moh'd Al-Ajouri, Alaa Univ Tabuk Dept Comp Sci Fac Comp & Informat Technol Tabuk 71491 Saudi Arabia

Coverage, one of the most important performance metrics for wireless sensor networks, reflects on how well a sensor field is monitored. Coverage problem is a devoted study of a node placement optimization problem in the coverage configuration before network deployment, where the objective is to find the optimal locations to place sensor nodes, such that the number of nodes (or the network cost) can be minimized and the coverage requirements can be satisfied. In this paper, we propose a harmony search (HS)-based deployment algorithm that can locate the optimal number of sensor nodes as well as their optimal locations for maximizing the network coverage and minimizing the network cost. The ability of HS is modified to automatically evolve the appropriate number of sensor nodes as well as their optimal locations. This can be accomplished by integrating the concept of adaptable length encoding in each solution vector to represent a variable number of candidate sensor nodes. Network coverage ratio, number of sensor nodes, and minimum distance between sensor nodes are the chief elements of a new objective function that has been offered to confirm the choice of the optimal number of sensor nodes and their positions. Experimental results show the ability of the proposed algorithm to find the appropriate number of sensor nodes and their locations. Furthermore, a comparative study with a metaheuristic Genetic-based algorithm and a random deployment technique has also been conducted and its results confirm the superiority of the proposed algorithm.

关键词： Wireless sensor networks node placement network cost coverage problem harmony search algorithm

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Application of harmony search algorithm to Reservoir Operation Optimization

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WATER RESOURCES MANAGEMENT 2015年第15期29卷 5729-5748页

作者： Bashiri-Atrabi, Hamid Qaderi, Kourosh Rheinheimer, David E. Sharifi, Erfaneh Shahid Bahonar Univ Kerman Dept Water Engn Kerman 76169133 Iran Univ Calif Merced Sch Engn Merced CA 95343 USA

In this study, a meta-heuristic technique called harmony search (HS) algorithm is developed for reservoir operation optimization with respect to flood control. The HS algorithm is used to minimize the water supply deficit and flood damages downstream of a reservoir. The GIS database is used to determine the flood damage functions. The efficacy of HS algorithm is evaluated in comparison with other techniques by using a benchmark problem for a single reservoir operation optimization problem. HS showed promising results in terms of speed of convergence to an optimal objective function value compared with other techniques such as honey-bee mating optimization (HBMO) and a global optimization model (LINGO 8.0 NLP solver). The HS algorithm is then applied to the Narmab reservoir, north of Iran, as a case study. Narmab reservoir serves multiple purposes including irrigation, flood control, and drinking water requirements. The developed model is applied for monthly operation. The results show that the HS algorithm can be effectively used for operation of reservoir for flood management.

关键词： Flood damages Reservoir operation optimization harmony search algorithm GIS HEC-RAS

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Dynamic parameter adaptation in the harmony search algorithm for the optimization of interval type-2 fuzzy logic controllers

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SOFT COMPUTING 2020年第1期24卷 179-192页

作者： Valdez, Fevrier Peraza, Cinthia Tijuana Inst Technol Tijuana 22379 Mexico

At the present time there are several types of metaheuristics which have been used to solve various types of problems in the real world. These metaheuristics contain parameters that are usually fixed throughout the iterations. However, various techniques exist to adjust the parameters of an algorithm such as probabilistic, fuzzy logic, among others. This work describes the methodology and equations for building Triangular and Gaussian interval type-2 membership functions, and this methodology was applied to the optimization of a benchmark control problem with an interval type-2 fuzzy logic controller. To validate in the best way the effect of uncertainty we perform experiments using noise (Pulse generator) and without noise. Also, a statistical z-test is presented to verify the effectiveness of the proposed method. The main contribution of this article is the proposed use of the theory of interval type-2 fuzzy logic to the dynamic adjustment of parameters for the harmony search algorithm and then its application to the optimal design of interval type-2 fuzzy logic controller.

关键词： Dynamic parameter adaptation harmony search algorithm Fuzzy controller Uncertainty Interval type-2 fuzzy logic

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Tasks assigning and sequencing of multiple AGVs based on an improved harmony search algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第11期10卷 4533-4546页

作者： Li, Guomin Li, Xinyu Gao, Liang Zeng, Bing Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

The efficiency of automatic guided vehicle (AGV) scheduling is important to improve the productivity of manufacturing enterprises. In this paper, the production materials and cutting tools consumables are transferred by multiple AGVs and a multi-objective mathematical model of AGV scheduling is established, which contains three objectives, i.e., the total travel distance of AGVs, the standard deviation of AGVs workload and the standard deviation of the difference between the latest delivery time and the predicted time of tasks. Then, an improved harmony search (HS) algorithm is proposed by adopting dynamic changing harmony memory considering rate (HMCR) parameters and implementing neighborhood search strategy for the best harmony in harmony memory (HM). Meanwhile, the harmony is divided into several segments according to the capacitated multiple-load AGVs. Each segment corresponds to the tasks execution scope of AGVs that return to the warehouse in turn. And the elements sequence of each segment represents the order of these tasks performed by AGV. At the same time, calculating the fitness value in each segment of harmony, and finally adding them up as the total fitness value of the whole harmony. A larger-scale instance from the real-life manufacturing enterprise is used to evaluate the performance of the proposed HS algorithm. The computational results show that the proposed HS algorithm outperforms the current solution.

关键词： Multiple-load AGVs Assigning Sequencing harmony search algorithm Dispatching

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PWR loading pattern optimization using harmony search algorithm

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ANNALS OF NUCLEAR ENERGY 2013年 53卷 288-298页

作者： Poursalehi, N. Zolfaghari, A. Minuchehr, A. Shahid Beheshti Univ Dept Engn GC Tehran Iran

In this paper a core reloading technique using harmony search, HS, is presented in the context of finding an optimal configuration of fuel assemblies, FA, in pressurized water reactors. To implement and evaluate the proposed technique a harmony search along Nodal Expansion Code for 2-D geometry, HSNEC2D, is developed to obtain nearly optimal arrangement of fuel assemblies in PWR cores. This code consists of two sections including harmony search algorithm and Nodal Expansion modules using fourth degree flux expansion which solves two dimensional-multi group diffusion equations with one node per fuel assembly. Two optimization test problems are investigated to demonstrate the HS algorithm capability in converging to near optimal loading pattern in the fuel management field and other subjects. Results, convergence rate and reliability of the method are quite promising and show the HS algorithm performs very well and is comparable to other competitive algorithms such as Genetic algorithm and Particle Swarm Intelligence. Furthermore, implementation of nodal expansion technique along HS causes considerable reduction of computational time to process and analysis optimization in the core fuel management problems. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： harmony search algorithm Nodal expansion method Fitness function Loading pattern optimization

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