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A Metaheuristic Optimization Approach for Tuning of Fractional-Order PID Controller for Speed Control of Sensorless BLDC Motor

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2018年第8期27卷

作者： Vanchinathan, K. Valluvan, K. R. Velalar Coll Engn & Technol Dept Elect & Elect Engn Erode 638012 Tamil Nadu India Velalar Coll Engn & Technol Dept Elect & Commun Engn Erode 638012 Tamil Nadu India

This paper deals with a novel method for bat algorithm (BA) based on optimal tuning of Fractional-Order Proportional Integral Derivative (FOPID) controller for governing the rotor speed of sensorless Brushless Direct Current (BLDC) motor. The BA is used for developing a novel optimization algorithm which can generate five degrees of freedom parameters namely K-p, K-i, K-d, lambda and mu of FOPID controller. The desired speed control and robust performance are achieved by using the FOPID closed loop speed controller with the help of BA for optimal tuning. The time domain specifications of a dynamic system for unit step input to FOPID controller for speed response such as peak time (t(r)), Percentage of overshoot (PO), settling time (t(s)), rise time (t(r)) have been evaluated and the steady-state error (e(ss)) of sensorless speed control of BLDC motor has been measured. The simulation results are compared with Artificial Bee Colony (ABC) optimization method and Modified Genetic algorithm (MGA) for evaluation of transient and steady state time domain characteristics. The proposed BA-based FOPID controller optimization technique is more efficient in improving the transient characteristic performance and reducing steady state error.

关键词： Sensorless BLDC motor drive optimal control fractional-order PID controller bat algorithm

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Adapting Code Maintainability to bat-inspired Test Case Prioritization

Adapting Code Maintainability to Bat-inspired Test Case Prio...

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IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

作者： Ozturk, Muhammed Maruf Engn Fac Dept Comp Engn Isparta Turkey

ISBN: (纸本)9781509057955

Time and budget constraints in developing a software create an adverse effect in terms of the adequacy of maintenance and test processes. This case can be considered as a burden for persons who account for test processes. In order to alleviate this burden, test case prioritization is one of the solutions. A nature-inspired method namely BITCP, which was developed based on bat algorithm, produced promising results. However, this method does not involve test case elements with respect to the code maintainability. In this work, the correlation between some code maintainability indicators including WMC, LCOM, and Coupling and cyclomatic complexity is investigated. IMPBITCP appears after adapting the results of the investigation to BITCP. The method is then compared with well known alternatives such as greedy-search, particle swarm optimization, and BITCP. The experiment involving six open source project showed that IMPCBITCP outperformed the others with respect to the APFD. The findings of the work indicates that if the factors affecting code maintenance are considered while developing test case prioritization techniques, APFD results becomes high and stable.

关键词： bat algorithm test case prioritization nature inspired methods

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Optimal Sizing and Management of Hybrid Renewable Energy System for Highways Lighting

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第4期8卷 2336-2349页

作者： Shaneh, Mahdi Shahinzadeh, Hossein Moazzami, Majid Gharehpetian, Gevork B. Islamic Azad Univ Dept Elect Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Najafabad Branch Smart Microgrid Res Ctr Najafabad Iran Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran

The energy consumption for the purpose of lighting has comprised about 30% of energy consumption and around 45% to 50% of peak load in Iran. Based on the statistics of the road maintenance and transportation organization of Iran (RMTO), the quantity of installed lighting equipment had significant growth in recent years. An appropriate combination of renewable energy resources leads to reliable, green and economic generation system. Using hybrid solar-wind systems to supply street lightings' required energy with low-power lamps is an impressive method to decrease energy consumption. In this paper, a comprehensive study is carried out to achieve an optimal design for a hybrid solar-wind system that supplies electricity to the roads and highways lights in Iran. In the proposed approach, to achieve an optimum design for the hybrid system, suitable options for each part of the system is selected according to its technical specifications, and then the capacity and the number of components is determined in order to minimize an objective function comprised of a cost function, loss of power supply probability (LPSP) and subject to satisfy prevailing technical constraints. bat algorithm (BA) is employed to solve the proposed optimization problem. Simulation results show the effectiveness of the proposed method in various situations of operation for the case study of Tabriz city regard to pertaining load profile and weather data. The case study is categorized into three scenarios in which the hybrid system has different schemes, and the results show that the proposed hybrid system is notably practical and cost-effective to supply electrical energy of highway and street lightings.

关键词： Hybrid Energy System bat algorithm Renewable Energy Optimization Street Lightings Energy Cost

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A Hybrid BA-ELM Model Based on Factor Analysis and Similar-Day Approach for Short-Term Load Forecasting

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ENERGIES 2018年第5期11卷 1282-1282页

作者： Sun, Wei Zhang, Chongchong North China Elect Power Univ Dept Business Adm Baoding 071000 Peoples R China

Accurate power-load forecasting for the safe and stable operation of a power system is of great significance. However, the random non-stationary electric-load time series which is affected by many factors hinders the improvement of prediction accuracy. In light of this, this paper innovatively combines factor analysis and similar-day thinking into a prediction model for short-term load forecasting. After factor analysis, the latent factors that affect load essentially are extracted from an original 22 influence factors. Then, considering the contribution rate of history load data, partial auto correlation function (PACF) is employed to further analyse the impact effect. In addition, ant colony clustering (ACC) is adopted to excavate the similar days that have common factors with the forecast day. Finally, an extreme learning machine (ELM), whose input weights and bias threshold are optimized by a bat algorithm (BA), hereafter referred as BA-ELM, is established to predict the electric load. A simulation experience using data deriving from Yangquan City shows its effectiveness and applicability, and the result demonstrates that the hybrid model can meet the needs of short-term electric load prediction.

关键词： short-term load forecasting factor analysis ant colony clustering extreme learning machine bat algorithm

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Using bio-inspired approaches to improve the quality of service in a multicast routing

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2017年第2期19卷 186-213页

作者： Mahseur, Mohammed Boukra, Abdelmadjid Univ Sci & Technol Houari Boumediene Fac Elect & Informat Dept Informat Algiers 16025 Algeria

This paper deals with the problem optimising the quality of service (QoS) in a multicast routing with multiple constraints (multi constrained least-cost multicast routing problem). To solve this problem, we propose two approaches. The first one named biogeography-based optimisation (BBO) is inspired by the biogeography mathematics;the second, named bat algorithm (BA) is inspired from bat behaviour. We propose to use a tree structure to represent the solution. We start optimising the graph representing the network. We propose to generate the initial population with greedy heuristic using depth first search (DFS). Then, we optimise the initial population solutions in order to minimise the execution time. We have adapted BBO and BA operators to deal with the discrete nature of the problem and the structures representing the problem solution. In experiments, we have used Waxman topology model to generate the graph. The results were encouraging in comparison with other existing algorithms.

关键词： multicast routing quality of service QoS BBO algorithm migration mutation bat algorithm echolocation optimisation metaheuristic

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Optimization of the box section of the main girder of the single-girder bridge crane by applying biologically inspired algorithms

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ENGINEERING STRUCTURES 2017年 148卷 452-465页

作者： Savkovic, Mile M. Bulatovic, Radovan R. Gasic, Milomir M. Pavlovic, Goran V. Stepanovic, Aleksandar Z. Univ Kragujevac Fac Mech & Civil Engn Kraljevo Dositejeva 19 Kraljevo 36000 Serbia Lola Inst Kneza Viseslava 70A Belgrade 11030 Serbia

The paper considers the problem of optimization of the box section of the main girder of the single-girder bridge crane. Reduction of the girder mass is set as the objective function. The constraint functions are the criteria of strength, local stability of plates, lateral stability of the girder, stiffness, dynamic stiffness, as well the constraints of cost effective design. Methods of the following biologically inspired algorithms were used for the optimization: Firefly algorithm (FA), bat algorithm (BA) and Cuckoo Search algorithm (CS). The obtained results of optimization were compared with several solutions of single-girder bridge cranes, which verified the optimization method. The paper presents the advantages and weaknesses of the methods applied to the concrete solutions of single-girder bridge cranes. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Box section Single-girder bridge crane Optimization Firefly algorithm bat algorithm Cuckoo Search algorithm

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Antlion algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第20期45卷 2304-2317页

作者： Premkumar, Kamaraj Manikandan, Bairavan Veerayan Kumar, Chellappan Agees Rajalakshmi Engn Coll Dept Elect & Elect Engn Madras Tamil Nadu India Mepco Schlenk Engn Coll Dept Elect & Elect Engn Sivakasi Tamil Nadu India Arunachala Coll Engn Women Dept EEE Vellichanthai Tamil Nadu India

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (), dynamic factor (), and number nodes (N-i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.

关键词： ant colony optimization antlion optimization bat algorithm brushless DC motor genetic algorithm particle swarm optimization recurrent fuzzy neural network controller

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Nature inspired algorithms for solving the community detection problem

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LOGIC JOURNAL OF THE IGPL 2017年第6期25卷 902-914页

作者： Hussein, Eslam Hafez, Ahmed Ibrahem Hassanien, Aboul Ella Fahmy, Aly A. Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Cairo Egypt Minia Univ Fac Comp & Informat Al Minya Egypt

Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the bat algorithm (BA) and Artificial Fish Swarm algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

关键词： Community detection community structure social networks bat algorithm Artificial Fish Swarm algorithm nature-inspired algorithms

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Optimization studies in high speed turning of Ti-6Al-4V

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APPLIED SOFT COMPUTING 2017年 51卷 105-115页

作者： D'Mello, Grynal Pai, P. Srinivasa Puneet, N. P. NMAM Inst Technol Dept Mech Engn Nitte 574110 Karnataka India Visveswaraya Technol Univ Belagavi India Srinivas Inst Technol Dept Mech Engn Mangalore 574143 Karnataka India

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm(BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R-a and R-t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Ti-6Al-4V Surface roughness Particle swarm optimization bat algorithm Firefly algorithma

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Analysis of FPA and BA meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2017年第7期11卷 817-828页

作者： Ghosh, Saradindu Panigrahi, Pratap K. Parhi, Dayal R. Natl Inst Technol Dept Elect Engn Durgapur 713209 W Bengal India Padmanava Coll Engn Dept Elect Engn Rourkela 769002 Odisha India Natl Inst Technol Dept Mech Engn Rourkela 769008 Odisha India

This study proposes the design of two efficient nature inspired intelligent optimal controllers, flower pollination algorithm (FPA) and bat algorithm (BA) for obtaining optimal path using an autonomous mobile robot in an unknown environment. FPA is based on the pollination process of flowering plants, which transfer pollens by using different pollinators. On the contrary, BA depends on echolocation and frequency tuning to solve different types of optimisation problems in engineering. To accomplish the path-planning task of mobile robot autonomously, a fitness function has been introduced considering the distance between robot-obstacle and robot-goal to satisfy the conditions of obstacle avoidance and goal reaching behaviour of robot. Based on the values of objective function of the algorithms, mobile robot avoids obstacles in the unknown environment and moves towards the goal. In this work, the efficiency of such controllers is verified using some simulations in MATLAB environment. Further, an experimental work is carried out in real-world environment using ARDUINO Mega 2560 microcontroller to ascertain the path length, travelling time and convergence speed of the two algorithms.

关键词： mobile robots clutter path planning optimal control intelligent robots mechanoception bioacoustics collision avoidance microcontrollers BA metaheuristic controller analysis FPA metaheuristic controller analysis optimal path planning cluttered environment nature inspired intelligent optimal controller flower pollination algorithm bat algorithm autonomous mobile robot echolocation frequency tuning optimisation problem robot-obstacle robot-goal obstacle avoidance MATLAB environment ARDUINO Mega 2560 microcontroller

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