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Security optimal power flow considering loading margin stability using hybrid FFA-PS assisted with brainstorming rules

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APPLIED SOFT COMPUTING 2015年 35卷 291-309页

作者： Mahdad, Belkacem Srairi, K. Biskra Univ Dept Elect Engn Biskra Algeria

This paper presents a new power system planning strategy which combines firefly algorithm (FFA) with pattern search algorithm (PS). The purpose is minimizing total fuel cost, total power loss and reducing total voltage deviation, with the objective of enhancing the loading margin stability and consequently the power system security. A new interactive and simple mechanism, inspired in brainstorming process, is proposed that allows FFA and PS algorithms to explore new regions of the search space. In this study the Static VAR compensator (SVC) is modeled and integrated in an efficient location which is chosen considering the voltage stability index. The proposed algorithm is interactive and tries to optimize a set of control variables at the same time, namely, active power generations, voltage of generators, tap transformers, and the reactive power of shunt compensators to optimize three objective functions such as: fuel cost, total power loss and total voltage deviation. These variables are optimized using a flexible interactive and competitive search mechanism. The proposed planning strategy has been examined and applied to two practical test systems IEEE 14-Bus and IEEE 30-Bus. Simulation results confirm the effectiveness of this hybrid strategy for solving the security optimal power flow. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Optimal power flow firefly algorithm Pattern search Brainstorming process Objective functions SVC

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An approach for the global reliability based optimization of the size and shape of truss structures

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MECHANICS & INDUSTRY 2015年第6期16卷 1-11页

作者： Lopez, Rafael Holdorf Torii, Andre Jacomel Fadel Miguel, Leandro Fleck De Cursi, Jose Eduardo Souza Univ Fed Santa Catarina Dept Civil Engn CORE BR-8804097 Florianopolis SC Brazil Univ Fed Paraiba Dept Comp Sci CORE BR-8804097 Joao Pessoa Paraiba Brazil Inst Natl Sci Appl Rouen Dept Mecan F-76800 St Etienne France

This paper presents a methodology to perform global reliability based design optimization (RBDO) of the size and shape of truss structures. This methodology is comprised by the use of a global constraint and the response surface method to deal with the reliability analysis together with the firefly algorithm (FA) to carry out the structural optimization. The former is responsible for the reduction of the computational cost required in the evaluation of the probabilistic constraints. The latter overcomes the issues related to the non-convexity and mixed-variables of the optimization problem. Two examples are analysed in order to show the effectiveness of the methodology. In these examples, the FA is compared to other two well-known algorithms, the harmony search (HS) and genetic algorithm (GA). As a result, the FA reached the best performance in the examples analysed. All the optima found were checked using a classical first order reliability method (FORM) approach, validating the results provided by the response surface method.

关键词： Truss optimization firefly algorithm reliability based design optimization size optimization shape optimization global optimization

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Bio-inspired approach to learning robot motion trajectories and visual control commands

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第5期42卷 2624-2637页

作者： Mitic, Marko Miljkovic, Zoran Univ Belgrade Fac Mech Engn Dept Prod Engn Belgrade 11120 35 Serbia

In this paper, a novel bio-inspired learning control approach (BILCA) for mobile robots based on Learning from Demonstration (LfD), firefly algorithm (FA), and homography between current and target camera view is developed. BILCA consists of two steps: (i) first step in which the actuator commands are learned using FA and demonstrations of desired behavior, and (ii) second step in which the obtained wheel commands are evaluated through the real world experiment. Two different problems are considered in this study: trajectory reproduction, and generation of visual control commands for correction of robot orientation. Developed simulations are used to evaluate BILCA in the domain of learning actuator commands for reproduction of different complex trajectories. Results show that the bigger firefly swarms produce better results in terms of accuracy in the final mobile robot pose, and that the desired trajectory is reproduced with minimal error in final control iteration. Likewise, simulations prove that the FA outperforms other metaheuristic techniques. Experiment conducted on a real mobile robot in indoor environment unifies two considered problems within a single transportation task. Depending of the feature position in the image plane, the homography controller for forward motion or the BILCA based controller for robot orientation correction is employed. Experimental results show the applicability and effectiveness of the developed intelligent approach in real world conditions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： firefly algorithm Mobile robot Learning from Demonstration Visual control Homography Particle Swarm Optimization Bat algorithm

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Incomplete gravity compensator for a 4-DOF manipulator

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2015年第10期29卷 4417-4426页

作者： Kim, Sang-Hyung Cho, Chang-Hyun Chosun Univ Dept Control & Instruments Kwangju 501759 South Korea Chosun Univ Dept Mech & Syst Kwangju 501759 South Korea

This paper proposes gravity compensators for a 4-degree-of-freedom (4-DOF) humanlike manipulator. Eighteen springs (or 1-DOF gravity compensators) are required to achieve complete static balancing of a 4-DOF manipulator. Because locating 18 springs is impractical, incomplete gravity compensators are designed for practical implementation in this paper. Springs are selected using an objective function of the gravity compensation and design cost. The design cost indicates the complexity of the mechanisms. As a result, four- and two-spring designs are obtained. Optimizations of spring constants of the four- and two-spring designs are conducted for the objective function of gravity compensation. The torque ratios for the four-spring design are computed as [18.64%, 11.92%, 77.68%, 81.14%]. The torque ratios for the two-spring design are computed as [16.03%, 20.22%, 100.00%, 100.00%] and indicate that gravity compensation is made only at proximal joints to the base. Dynamic simulations are conducted, and simulation results show that the ratios of gravity compensation are achievable.

关键词： Gravity compensation Static balancing 1-DOF gravity compensator Bevel gears Manipulator firefly algorithm

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A BIM-based approach for automated tower crane layout planning

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AUTOMATION IN CONSTRUCTION 2015年 59卷 168-178页

作者： Wang, Jun Zhang, Xuedong Shou, Wenchi Wang, Xiangyu Xu, Bo Kim, Mi Jeong Wu, Peng Curtin Univ Australasia Joint Res Ctr Bldg Informat Modelling Bentley WA Australia Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu Peoples R China Kyung Hee Univ Dept Housing & Interior Design Seoul South Korea Chongqing Univ Sch Construct Management & Real Estate Chongqing Peoples R China Cent Queensland Univ Sch Engn & Technol Rockhampton Qld 4702 Australia

Tower crane layout design and planning within construction site is a common construction technical issue, and is regarded as a complex combinatorial problem. Previous research focused on utilising either mathematical methods or visualisation tools to find an optimal tower crane layout plan. Both these two approaches require large amounts of manual data input by the layout planners, which is time-consuming and not very practical in industry. The purpose of this paper is to develop an integrated approach which combines Building Information Modelling (BIM) and firefly algorithm (FA) to automatically generate an optimal tower crane layout plan. Firstly, BIM is utilised to provide inputs for the mathematical model. Then the FA is used to determine the optimal locations of tower cranes and supply points. Finally, the optimal tower crane layout scheme will be visualised and evaluated through BIM-based simulation. A practical case is selected to demonstrate the proposed approach. The final result is promising and demonstrates the practical value of this approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Tower crane firefly algorithm Building Information Modelling (BIM) Site layout

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Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

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CONNECTION SCIENCE 2015年第4期27卷 340-357页

作者： Sheikhan, Mansour Arabi, Mahdi Abbasnezhad Gharavian, Davood Islamic Azad Univ South Tehran Branch Dept Elect Engn POB 11365-4435 Tehran Iran Shahid Beheshti Univ Dept Elect Engn Tehran Iran

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

关键词： emotion recognition speech processing modified Elman neural network optimisation gravitational search algorithm firefly algorithm

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Automatic test data generator: A tool based on search-based techniques

Automatic test data generator: A tool based on search-based ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruchika Malhotra Poornima Nitish Kumar Department of Computer Science Delhi Technological University Shahbad Daulatpur Bawana Road New Delhi

ISBN: (纸本)9781509014903

Software Testing is the most time consuming activity in the software development lifecycle. It is impossible to test everything. Hence, several automated test data generation techniques have been introduced in recent times in order to reduce the effort spent during testing. Search based techniques have been found to be more efficient than normal or random testing. In this paper, we propose to demonstrate the designing framework, implementation and explore the capabilities of a tool to aid in the generation of test data. Our tool is based on generating the optimal set of test cases based on the user defined coverage criteria. We have implemented the system in C++ language and have restricted ourselves to the use of command line interface. We provide the path as well as the test cases generated to the tester making his work of testing a lot easier.

关键词： firefly algorithm Automated test data generation Path based testing Control flow graphs

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Scalable Parallel Clustering Approach for Large Data Using Parallel K Means and firefly algorithms

Scalable Parallel Clustering Approach for Large Data Using P...

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International Conference on High Performance Computing and Applications (ICHPCA)

作者： Mathew, Juby Vijayakumar, R. Amaljyothi Coll Engn Dept MCA Kanjirappally Kerala India Mahatma Gandhi Univ Kottayam Kerala India

ISBN: (纸本)9781479959587

This paper mainly focuses in identifying the limitations of the k means algorithm and to propose the parallelization of the k-means using firefly based clustering method. The new parallel architecture can handle large number of clusters. firefly algorithm to find initial optimal cluster centroid and then k-means algorithm with optimized centroid to refined them and improve clustering accuracy. The final convergence issue is also addressed and solved to a great extent. Finally modified algorithm is compared with parallel k means is demonstrated with experiments and it has been found that the performance of modified algorithm is better than the existing algorithm. Four typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. To achieve this we can use fork/join method in java programming. It is the most effective design method for achieve good parallel performance

关键词： Clustering k-means parallel k-means firefly algorithm join and fork parallelism

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New progresses in swarm intelligence-based computation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第1期7卷 26-35页

作者： Duan, Haibin Luo, Qinan Beihang Univ BUAA Sch Automat Sci & Elect Engn State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China

Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

关键词： swarm intelligence SI bio-inspired computation ant colony optimisation ACO particle swarm optimisation PSO artificial bee colony ABC firefly algorithm FA bat algorithm BA pigeon inspired optimisation PIO

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Assessing Performance of Genetic and firefly algorithms for Optimal Design of Heat Exchangers

Assessing Performance of Genetic and Firefly Algorithms for ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Khosravi, Rihanna Khosravi, Abbas Nahavandi, Saeid Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

ISBN: (纸本)9781479938407

This paper aims to comprehensively investigate performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA) and firefly algorithm (FA) are implemented for finding the optimal values for seven key design variables of the STHX model. epsilon-NTU method and Bell-Delaware procedure are used for thermal modelling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA always lead to maximum STHX efficiency. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behaviour of the majority of decision variables remain consistent in different runs of the FA optimization process.

关键词： Heat exchanger optimization genetic algorithm firefly algorithm

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