The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is...The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.展开更多
Transformers are one of the main components of any power system. An accurate estimation of system be-haviour, including load flow studies, protection, and safe control of the system calls for an accurate equiva-lent c...Transformers are one of the main components of any power system. An accurate estimation of system be-haviour, including load flow studies, protection, and safe control of the system calls for an accurate equiva-lent circuit parameters of all system components such as generators, transformers, etc. This paper presents a methodology to estimate the equivalent circuit parameters of the Three Winding Transformer (TWT) using Bacterial Foraging Algorithm (BFA). The estimation procedure based on load test data at one particular op-erating point namely supply voltage, load currents, input power. The performance characteristics, such as efficiency and voltage regulation are considered along with the name plate data in order to minimize the er-ror between the estimated and measured data. The estimation procedure is demonstrated with a sample three winding transformer and the results are compared against the directly measured performance of TWT and genetic algorithm optimization results. The simulation results show the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the bacte-rial foraging algorithm.展开更多
In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the us...In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.展开更多
The electrical circuit equivalents of magnetic device structures such as transformer require an exact knowledge of its parameters. Efficient parameter estimation technique is essential to obtain the equivalent circuit...The electrical circuit equivalents of magnetic device structures such as transformer require an exact knowledge of its parameters. Efficient parameter estimation technique is essential to obtain the equivalent circuit parameters of transformer because the parameters are used to manipulate parasitic elements and to obtain the enhanced circuit performance. In this paper, Bacterial Foraging Algorithm (BFA) has been applied to estimate the equivalent circuit parameters of single phase core type transformer. The information of open Circuit (OC) and Short Circuit (SC) tests has been utilized in BFA algorithm. The effectiveness of the proposed approach has been tested with a sample transformer and the simulation results are compared against the conventional method. The numerical results show that the proposed approach outperforms the conventional method in the aspects of solution quality.展开更多
This paper proposes a boost inverter model capable of coping with changes in load as well as line parameters. In order to achieve an output AC voltage higher than the input DC voltage, we can use this model consisting...This paper proposes a boost inverter model capable of coping with changes in load as well as line parameters. In order to achieve an output AC voltage higher than the input DC voltage, we can use this model consisting of a pair of DC-DC converters with a load connected differentially across them. This paper aims at developing a boost inverter that is capable of achieving a very high gain, to obtain an AC voltage of 110 Vrms from a DC input of 36 V. This is exceptionally beneficial in renewable energy applications, where the input voltage garnered is quite small, and in need of stepping up for commercial use or transmission. However, aside from the voltage level itself, lowering the rise time, settling time, peak overshoot and steady state error of the system is of cardinal importance in order to maintain a reliable output voltage. Closed loop control of the differentially connected DC-DC converters is necessary to determine the optimal stable operating point. This paper addresses the above concerns through optimization of the proportional and integral constants using the novel Bacterial Foraging Algorithm, ensuring operation at the required optimal stable operating point. Moreover, load/line disturbances may occur due to which the stability of output voltage may be compromised and THD value may increase to undesirable extents. In these cases, utilization of the output voltage is no longer viable for several applications sensitive to such voltage fluctuations. We have demonstrated that our proposed model is capable of restoring/reverting to the satisfactory sinusoidal waveform fashion within a single voltage cycle. The waveform results that demonstrate the resilience of our model to such disturbances are represented appropriately.展开更多
The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the ...The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.展开更多
Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial...Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this algorithm.The superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.展开更多
In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formu...In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.展开更多
A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are...A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the quality of the reconstructed images significantly.展开更多
基金supported by the National Natural Science Foundation of China(6137415361473138)+2 种基金Natural Science Foundation of Jiangsu Province(BK20151130)Six Talent Peaks Project in Jiangsu Province(2015-DZXX-011)China Scholarship Council Fund(201606845005)
文摘The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.
文摘Transformers are one of the main components of any power system. An accurate estimation of system be-haviour, including load flow studies, protection, and safe control of the system calls for an accurate equiva-lent circuit parameters of all system components such as generators, transformers, etc. This paper presents a methodology to estimate the equivalent circuit parameters of the Three Winding Transformer (TWT) using Bacterial Foraging Algorithm (BFA). The estimation procedure based on load test data at one particular op-erating point namely supply voltage, load currents, input power. The performance characteristics, such as efficiency and voltage regulation are considered along with the name plate data in order to minimize the er-ror between the estimated and measured data. The estimation procedure is demonstrated with a sample three winding transformer and the results are compared against the directly measured performance of TWT and genetic algorithm optimization results. The simulation results show the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the bacte-rial foraging algorithm.
文摘In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.
文摘The electrical circuit equivalents of magnetic device structures such as transformer require an exact knowledge of its parameters. Efficient parameter estimation technique is essential to obtain the equivalent circuit parameters of transformer because the parameters are used to manipulate parasitic elements and to obtain the enhanced circuit performance. In this paper, Bacterial Foraging Algorithm (BFA) has been applied to estimate the equivalent circuit parameters of single phase core type transformer. The information of open Circuit (OC) and Short Circuit (SC) tests has been utilized in BFA algorithm. The effectiveness of the proposed approach has been tested with a sample transformer and the simulation results are compared against the conventional method. The numerical results show that the proposed approach outperforms the conventional method in the aspects of solution quality.
文摘This paper proposes a boost inverter model capable of coping with changes in load as well as line parameters. In order to achieve an output AC voltage higher than the input DC voltage, we can use this model consisting of a pair of DC-DC converters with a load connected differentially across them. This paper aims at developing a boost inverter that is capable of achieving a very high gain, to obtain an AC voltage of 110 Vrms from a DC input of 36 V. This is exceptionally beneficial in renewable energy applications, where the input voltage garnered is quite small, and in need of stepping up for commercial use or transmission. However, aside from the voltage level itself, lowering the rise time, settling time, peak overshoot and steady state error of the system is of cardinal importance in order to maintain a reliable output voltage. Closed loop control of the differentially connected DC-DC converters is necessary to determine the optimal stable operating point. This paper addresses the above concerns through optimization of the proportional and integral constants using the novel Bacterial Foraging Algorithm, ensuring operation at the required optimal stable operating point. Moreover, load/line disturbances may occur due to which the stability of output voltage may be compromised and THD value may increase to undesirable extents. In these cases, utilization of the output voltage is no longer viable for several applications sensitive to such voltage fluctuations. We have demonstrated that our proposed model is capable of restoring/reverting to the satisfactory sinusoidal waveform fashion within a single voltage cycle. The waveform results that demonstrate the resilience of our model to such disturbances are represented appropriately.
文摘The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters.
基金supported by the National Natural Science Foundation of China(61102106)the China Postdoctoral Science Foundation(2013M530148)+1 种基金the Heilongjiang Postdoctoral Fund(LBH-Z13054)the Fundamental Research Funds for the Central Universities(HEUCF140809)
文摘Parameter adjustment that maximizes the energy efficiency of cognitive radio networks is studied in this paper where it can be investigated as a complex discrete optimization problem. Then a quantum-inspired bacterial foraging algorithm(QBFA)is proposed. Quantum computing has perfect characteristics so as to avoid local convergence and speed up the optimization of QBFA. A proof of convergence is also given for this algorithm.The superiority of QBFA is verified by simulations on three test functions. A novel parameter adjustment method based on QBFA is proposed for resource allocation of green cognitive radio. The proposed method can provide a globally optimal solution for parameter adjustment in green cognitive radio networks. Simulation results show the proposed method can reduce energy consumption effectively while satisfying different quality of service(Qo S)requirements.
文摘In this paper, the objective of minimum load balancing index （LBI） for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm （BFOA）. The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.
基金Supported by the National Natural Science Foundation of China (No.60572100)by the Royal Society (U.K.) International Joint Projects 2006/R3-Cost Share with NSFC (No.60711130233)
文摘A novel Bacterial Foraging Algorithm (BFA) based neural network is presented for image compression. To improve the quality of the decompressed images, the concepts of reproduction, elimination and dispersal in BFA are firstly introduced into neural network in the proposed algorithm. Extensive experiments are conducted on standard testing images and the results show that the pro- posed method can improve the quality of the reconstructed images significantly.
