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Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation

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ENERGY CONVERSION AND MANAGEMENT 2014年 81卷 504-519页

作者： Tian, Hao Yuan, Xiaohui Ji, Bin Chen, Zhihuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

This paper proposes a non-dominated sorting gravitational search algorithm with chaotic mutation (NSGSA-CM) to solve short-term economic/environmental hydrothermal scheduling (SEEHTS) problem. The SEEHTS problem is formulated as a multi-objective optimization problem with many equality and inequality constraints. By introducing the concept of non-dominated sorting and crowding distance, NSGSA-CM can optimize two objectives of fuel cost and pollutant emission simultaneously and obtain a set of Pareto optimal solutions in one trial. In order to improve the performance of NSGSA-CM, the paper introduces particle memory character and population social information in velocity update process. And a chaotic mutation is adopted to prevent the premature convergence. Furthermore, NSGSA-CM utilizes an elitism strategy which selects better solutions in parent and offspring populations based on their non-domination rank and crowding distance to update new generations. When dealing with the constraints of the SEEHTS, new strategies without penalty factors are proposed. In order to handle the water dynamic balance and system load balance constraints, this paper uses a combined strategy which adjusts the violation averagely to each decision variable at first and adjusts the rest violation randomly later. Meanwhile, a new symmetrical adjustment strategy by modifying the discharges at current and later interval without breaking water dynamic balance is adopted to handle reservoir storage constraints. To test the performance of the proposed NSGSA-CM, simulation results are compared with other methods reported in literatures. The results verify that the proposed NSGSA-CM is feasible and efficient for solving SEEHTS problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Economic/environmental scheduling gravitational search algorithm Non-dominated sorting Constraints handling Chaotic mutation Pareto optimal solutions

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Automatic cluster evolution using gravitational search algorithm and its application on image segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 29卷 93-103页

作者： Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh JCDMCOE CSE Dept Sirsa Haryana India NIT Dept Comp Engn Kurukshetra Haryana India GJUS & T CSE Dept Hisar Haryana India

In real life problems, prior information about the number of clusters is not known. In this paper, an attempt has been made to determine the number of clusters using automatic clustering using gravitational search algorithm (ACGSA). Based on the statistical property of datasets, two new concepts are proposed to efficiently find the optimal number of clusters. Within the ACGSA, a variable chromosome representation is used to encode the cluster centers with different number of clusters. In order to refine cluster centroids, two new operations namely threshold setting and weighted cluster centroid computation are also introduced. Finally, a new fitness function is proposed to make the search more efficient. A comparison of the proposed technique is also carried out with automatic clustering techniques developed recently. The proposed technique is further applied for automatic segmentation of both grayscale and color images and its performance is compared with other techniques. Experimental results demonstrate the efficiency and efficacy of the proposed clustering technique over other existing techniques. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Clustering Image segmentation

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Application of an effective modified gravitational search algorithm for the coordinated scheduling problem in a two-stage supply chain

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第1-4期70卷 335-348页

作者： Pei, Jun Liu, Xinbao Pardalos, Panos M. Fan, Wenjuan Yang, Shanlin Wang, Ling Hefei Univ Technol Sch Management Hefei Peoples R China Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei Peoples R China N Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

This paper investigates a products and vehicles scheduling problem in a two-stage supply chain environment, where jobs first need to be processed on the serial batching machines of multiple manufacturers distributed in various geographic zones and then transported by vehicles to a customer for further processing. The size and processing time of jobs are varying with the difference of types, and each batch takes a setup time before being processed. The problem of minimizing the makespan is formalized as a mixed integer programming model and proved to be NP-hard. In addition, the structural properties and lower bound of the problem are analyzed and inferred. Then a modified gravitational search algorithm (MGSA) is proposed to solve the problem. In the developed MGSA, several improvement strategies and the batching mechanism DP-H are introduced. The effectiveness and efficiency of the proposed MGSA are demonstrated and compared with a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA). Besides, the error ratios between the lower bound and the best found solutions are reported. The experimental results indicate that the proposed MGSA is more robust and outperforms PSO and GA on the studied two-stage supply chain scheduling problem.

关键词： Supply chain scheduling Batching gravitational search algorithm Lower bound Dynamic programming Heuristic algorithm

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Feature subset selection by gravitational search algorithm optimization

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INFORMATION SCIENCES 2014年 281卷 128-146页

作者： Han, XiaoHong Chang, XiaoMing Quan, Long Xiong, XiaoYan Li, JingXia Zhang, ZhaoXia Liu, Yi Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Sys Minist Educ China Taiyuan Shanxi Peoples R China

A new method for feature subset selection in machine learning, FSS-MGSA (Feature Subset Selection by Modified gravitational search algorithm), is presented. FSS-MGSA is an evolutionary, stochastic search algorithm based on the law of gravity and mass interactions, and it can be executed when domain knowledge is not available. A wrapper approach, over Naive-Bayes, ID3, K-Nearest Neighbor and Support Vector Machine learning algorithms, is used to evaluate the goodness of each visited solution. The key to the success of the MGSA is to utilize the piecewise linear chaotic map for increasing its diversity of species, and to use sequential quadratic programming for accelerating local exploitation. Promising results are achieved in a variety of tasks where domain knowledge is not available. The experimental results show that the proposed method has the ability of selecting the discriminating input features correctly and can achieve high accuracy of classification, which is comparable to or better than well-known similar classifier systems. Furthermore, the MGSA is tested on ten functions provided by CEC 2005 special session and compared with various modified gravitational search algorithm, Particle Swarm Optimization, and Genetic algorithm. The obtained results confirm the high performance of the MGSA in solving various problems in optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Feature subset selection Classification gravitational search algorithm Learning algorithm Sequential quadratic programming Chaotic map

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A quantum inspired gravitational search algorithm for numerical function optimization

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INFORMATION SCIENCES 2014年 267卷 83-100页

作者： Soleimanpour-moghadam, Mohadeseh Nezamabadi-pour, Hossein Farsangi, Malihe M. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel GSA using quantum mechanics theories to generate a quantum-inspired gravitational search algorithm (QIGSA). The application of quantum mechanics theories in the proposed QIGSA provides a powerful strategy to diversify the algorithm's population and improve its performance in preventing premature convergence to local optima. The simulation results and comparison with nine state-of-the-art algorithms confirm the effectiveness of the QIGSA in solving various benchmark optimization functions.(C) 2013 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence gravitational search algorithm Quantum computing Numerical function optimization

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gravitational search algorithm combined with chaos for unconstrained numerical optimization

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APPLIED MATHEMATICS AND COMPUTATION 2014年第0期231卷 48-62页

作者： Gao, Shangce Vairappan, Catherine Wang, Yan Cao, Qiping Tang, Zheng Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Tateyama Inst Syst Toyama 9300016 Japan Toyama Univ Grad Sch Innovat Life Sci Toyama 9308555 Japan Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

gravitational search algorithm (GSA) is the one of the newest developed nature-inspired heuristics for optimization problem. It is designed based on the Newtonian gravity and has shown excellent search abilities when applying it to optimization problems. Nevertheless, GSA still has some disadvantages such as slow convergence speed and local optima trapping problems. To alleviate these inherent drawbacks and enhance the performance of GSA, chaos, which is of ergodicity and stochasticity, is incorporated into GSA by two kinds of methods. One method uses chaos to generate chaotic sequences to substitute random sequences, while another one uses chaos to act as a local search approach. The resultant hybrid algorithms, called chaotic gravitation search algorithms (CGSA1 and CGSA2), thus reasonably have advantages of both GSA and chaos. Eight widely used benchmark numerical optimization problems are chosen from the literature as the test suit. Experimental results demonstrate that both CGSA1 and CGSA2 perform better than GSA and other five chaotic particle swarm optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： gravitational search algorithm Chaos Local search Performance Particle swarm optimization

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Piecewise function based gravitational search algorithm and its application on parameter identification of AVR system

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NEUROCOMPUTING 2014年 124卷 139-148页

作者： Li, Chaoshun Li, Hongshun Kou, Pangao Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Zhongnan Univ Econ & Law Wuhan 430074 Peoples R China Hunan Elect Power Co Sci Res Inst Changsha 410007 Hunan Peoples R China

Heuristic optimization has shown its superiority in handling identification problem of complicated system, for methods based on heuristic optimization do not have special requirements on model structures of the target system. In this paper, a piecewise function based gravitational search algorithm (PFGSA) is proposed and applied in parameter identification of automatic voltage regulator (AVR) system. In the proposed algorithm, a piecewise function is designed as the gravitational constant function to replace the traditional exponential equation. The piecewise function provides more rational gravitational constant to control the convergence of algorithm, and thus excellent searching ability is likely to be achieved. Moreover a new weighted objective function is proposed in the identification frame. Comparative experimental studies are conducted to test the searching ability of PFGSA and to verify the performance of proposed identification strategy, while genetic algorithm, particle swarm optimization and GSA are employed for comparison. The experimental results show that PFGSA performs the best on term of accuracy and stability in the parameter identification of AVR system, and the proposed identification strategy is effective. (C) 2013 Elsevier B.V. All rights reserved.

关键词： AVR system Parameter identification gravitational search algorithm Piecewise function

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Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode

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APPLIED ENERGY 2014年 128卷 164-174页

作者： Marzband, Mousa Ghadimi, Majid Sumper, Andreas Luis Dominguez-Garcia, Jose Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England Islamic Azad Univ Lahijan Branch Dept Elect Engn Lahijan Guilan Iran Catalonia Inst Energy Res IREC Barcelona 08930 Spain Univ Politecn Catalunya UPC EU Engn Tecn Ind Barcelona CITCEA UPC Dept Elect Engn Barcelona 08036 Spain Univ Politecn Cataluna Ctr Innovacio Tecnol Convertidors Estat & Acciona Dept Elect Engn ETS Engn Ind Barcelona E-08028 Barcelona Spain

Both performance optimization and scheduling of the distributed generation (DG) are relevant implementing an energy management system (EMS) within Microgrid (MG). Furthermore, optimization methods need to be applied to achieve maximum efficiency, improve economic dispatch as well as acquiring the best performance. This paper proposes an optimization method based on gravitational search algorithm to solve such problem in a MG including different types of DG units with particular attention to the technical constraints. This algorithm includes the implementation of some variation in load consumption model considering accessibility to the energy storage (ES) and demand response (DR). The proposed method is validated experimentally. Obtained results show the improved performance of the proposed algorithm in the isolated MG, in comparison with conventional EMS. Moreover, this algorithm which is feasible from computational viewpoint, has many advantages as peak consumption reduction, electricity generation cost minimization among other. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Day ahead scheduling Demand response Energy management system Microgrid Optimal operation and scheduling gravitational search algorithm

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Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 55卷 29-40页

作者： Shaw, Binod Mukherjee, V. Ghoshal, S. P. Asansol Engn Coll Dept Elect Engn Asansol W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India

gravitational search algorithm (GSA) is based on law of gravity and the interaction between masses. In GSA, searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. In this paper, to further improve the optimization performance of GSA, opposition-based learning is employed in opposition-based gravitational search algorithm (OGSA) for population initialization and also for generation jumping. In the present work, OGSA is applied for the solution of optimal reactive power dispatch (ORPD) of power systems. Traditionally, ORPD is defined as the minimization of active power transmission losses by controlling a number of control variables. ORPD is formulated as a non-linear constrained optimization problem with continuous and discrete variables. In this work, OGSA is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and amount of reactive compensation to optimize certain objectives. The study is implemented on IEEE 30-, 57- and 118-bus test power systems with different objectives that reflect minimization of either active power loss or that of total voltage deviation or improvement of voltage stability index. The obtained results are compared to those yielded by the other evolutionary optimization techniques surfaced in the recent state-of-the-art literature including basic GSA. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness and robustness for solving ORPD problems of power systems. (C) 2013 The Authors. Published by Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Opposition-based learning Optimal reactive power dispatch Power systems Optimization

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Optimal scheduling of electric vehicle ordered charging and discharging based on improved gravitational search and particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 157卷

作者： Pan, Kui Liang, Chuan-Dong Lu, Min Shihezi Univ Sch Mechano Elect Engn Shihezi 832003 Xinjiang Peoples R China

With the rapid growth of the number of Electric Vehicles (EVs), access to large-scale EVs will bring serious safety hazards to the operation planning of the power system. It needs to be supported by an effective EV charging and discharging behavior control strategy to meet the operation demand of the power system. An optimization model with the objectives of minimizing grid load variance and minimizing user charging cost is established. An improved hybrid algorithm is proposed for the optimal allocation of charging and discharging power of EVs by combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA). The performance of variant algorithm is tested using CEC2005 benchmarking functions sets and applied to the solution of the ordered charge-discharge optimal scheduling model. The results show that the convergence accuracy of the algorithm is better than the traditional algorithm, and it can effectively balance exploration and exploitation ability of the particles. In addition, the scheduling analysis is performed for different charging strategies of EVs. The scheduling results show that with the same optimization weights, implementing the ordered charging and discharging strategy can significantly reduce the charging cost of users and the load variance of the grid. Thus, the operational stability of the grid and the economic benefits for users are improved.

关键词： Electric vehicles Multi-objective optimization Power grid Particle swarm optimization gravitational search algorithm

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