To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The se...To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.展开更多
To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic i...To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual,and each original individual has its own symbiotic individual. Differential evolution( DE) operators are used to evolve the original population. And,particle swarm optimization( PSO) is applied to co-evolving the symbiotic population. Thus,with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functions. The results show that the average performance of PSODE is the best.展开更多
The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorith...The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm （EDA） using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature conver- gence. Two water distribution network benchmark exam- ples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum （6.081 MS） for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum （1.921M） than the current literature reported best minimum （1.923MC）. The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.展开更多
Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and...Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.展开更多
为分析智能软开关(soft open point,SOP)连续调节能力对柔性配电网(flexible distribution network,FDN)风险的影响。首先,实现基于三点估计的FDN风险评估方法;采用三点估计法结合交直流交替迭代法和Gram-Charlier级数展开法进行FDN概...为分析智能软开关(soft open point,SOP)连续调节能力对柔性配电网(flexible distribution network,FDN)风险的影响。首先,实现基于三点估计的FDN风险评估方法;采用三点估计法结合交直流交替迭代法和Gram-Charlier级数展开法进行FDN概率潮流计算,获得节点电压与支路有功功率的概率密度函数,使用越限偏移量结合风险偏好型效用函数构建严重度函数,根据风险评估理论建立并计算风险评估指标。其次,在此基础上,提出一种计及SOP参数优化的FDN风险评估方法;以系统总风险最低为目标,建立计及SOP参数优化的FDN风险评估模型,采用粒子群优化算法结合基于三点估计的FDN风险评估方法对其进行求解,用得到的结果去配置SOP,并对此FDN进行风险评估。以3个IEEE 33节点网络通过三端口SOP互联形成的FDN为例,验证了所提风险评估方法的有效性,分析了SOP连续调节能力以及不同接入位置对FDN风险的影响。展开更多
针对平面麦克风阵列的声源三维坐标估计问题,文中在TDOA(Time Difference of Arrival)声源定位算法中引入粒子群优化算法进行位置估计。利用PHAT(Phase Transform)加权函数的广义互相关法计算得到时延差的真实值,结合麦克风的坐标位置,...针对平面麦克风阵列的声源三维坐标估计问题,文中在TDOA(Time Difference of Arrival)声源定位算法中引入粒子群优化算法进行位置估计。利用PHAT(Phase Transform)加权函数的广义互相关法计算得到时延差的真实值,结合麦克风的坐标位置,通过几何关系计算出假设声源到达麦克风之间的时延差的估计值。设计时延真实值和估计值差值的平方和为粒子适应度函数,利用粒子群优化算法搜索空间中符合适应度函数的声源点,实现声源位置估计。仿真结果表明,在计算速度与球形插值法相近的情况下,文中所提算法比球形插值法具有更好的鲁棒性和抗噪性。展开更多
Maximum likelihood estimation(MLE)is an effective method for localizing radioactive sources in a given area.However,it requires an exhaustive search for parameter estimation,which is time-consuming.In this study,heuri...Maximum likelihood estimation(MLE)is an effective method for localizing radioactive sources in a given area.However,it requires an exhaustive search for parameter estimation,which is time-consuming.In this study,heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors.Hence,the time consumption of MLE would be effectively reduced.First,the radiation source was detected using the k-sigma method.Subsequently,the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source.A comparative study was performed in which the estimation accuracy and time consump-tion of the MLE were evaluated for traditional methods and heuristic techniques.The traditional MLE was performed via a grid search method using fixed and multiple resolutions.Additionally,four commonly used heuristic algorithms were applied:the firefly algorithm(FFA),particle swarm optimization(PSO),ant colony optimization(ACO),and artificial bee colony(ABC).The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program.The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE.The time consumption for the heuristic-based MLE was 0.75,0.03,0.02,and 0.059 s for FFA,PSO,ACO,and ABC,respectively.The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE.Hence,heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.展开更多
文摘To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.
基金National Key Basic Research Project of China(973 program)(No.2013CB733600)National Natural Science Foundation of China(No.21176073)+1 种基金Program for New Century Excellent Talents in University,China(No.NCET-09-0346)the Fundamental Research Funds for the Central Universities,China
文摘To implement self-adaptive control parameters,a hybrid differential evolution algorithm integrated with particle swarm optimization( PSODE) is proposed. In the PSODE, control parameters are encoded to be a symbiotic individual of original individual,and each original individual has its own symbiotic individual. Differential evolution( DE) operators are used to evolve the original population. And,particle swarm optimization( PSO) is applied to co-evolving the symbiotic population. Thus,with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the realtime optimum control parameters are obtained. The proposed algorithm is compared with some DE variants on nine functions. The results show that the average performance of PSODE is the best.
基金This work was supported by the National Science Foundation Award 0836046. The opinions expressed in this paper are solely those of the authors, and do not necessarily reflect the views of the funding agency.
文摘The optimization of a water distribution network （WDN） is a highly nonlinear, multi-modal, and constrained combinatorial problem. Particle swarm opti- mization （PSO） has been shown to be a fast converging algorithm for WDN optimization. An improved estimation of distribution algorithm （EDA） using historic best positions to construct a sample space is hybridized with PSO both in sequential and in parallel to improve population diversity control and avoid premature conver- gence. Two water distribution network benchmark exam- ples from the literature are adopted to evaluate the performance of the proposed hybrid algorithms. The experimental results indicate that the proposed algorithms achieved the literature record minimum （6.081 MS） for the small size Hanoi network. For the large size Balerma network, the parallel hybrid achieved a slightly lower minimum （1.921M） than the current literature reported best minimum （1.923MC）. The average number of evaluations needed to achieve the minimum is one order smaller than most existing algorithms. With a fixed, small number of evaluations, the sequential hybrid outperforms the parallel hybrid showing its capability for fast convergence. The fitness and diversity of the populations were tracked for the proposed algorithms. The track record suggests that constructing an EDA sample space with historic best positions can improve diversity control significantly. Parallel hybridization also helps to improve diversity control yet its effect is relatively less significant.
文摘Maximum likelihood estimation is a method of estimating the parameters of a statistical model in statistics. It has been widely used in a good many multi-disciplines such as econometrics, data modelling in nuclear and particle physics, and geographical satellite image classification, and so forth. Over the past decade, although many conventional numerical approximation approaches have been most successfully developed to solve the problems of maximum likelihood parameter estimation, bio-inspired optimization techniques have shown promising performance and gained an incredible recognition as an attractive solution to such problems. This review paper attempts to offer a comprehensive perspective of conventional and bio-inspired optimization techniques in maximum likelihood parameter estimation so as to highlight the challenges and key issues and encourage the researches for further progress.
文摘为分析智能软开关(soft open point,SOP)连续调节能力对柔性配电网(flexible distribution network,FDN)风险的影响。首先,实现基于三点估计的FDN风险评估方法;采用三点估计法结合交直流交替迭代法和Gram-Charlier级数展开法进行FDN概率潮流计算,获得节点电压与支路有功功率的概率密度函数,使用越限偏移量结合风险偏好型效用函数构建严重度函数,根据风险评估理论建立并计算风险评估指标。其次,在此基础上,提出一种计及SOP参数优化的FDN风险评估方法;以系统总风险最低为目标,建立计及SOP参数优化的FDN风险评估模型,采用粒子群优化算法结合基于三点估计的FDN风险评估方法对其进行求解,用得到的结果去配置SOP,并对此FDN进行风险评估。以3个IEEE 33节点网络通过三端口SOP互联形成的FDN为例,验证了所提风险评估方法的有效性,分析了SOP连续调节能力以及不同接入位置对FDN风险的影响。
文摘针对平面麦克风阵列的声源三维坐标估计问题,文中在TDOA(Time Difference of Arrival)声源定位算法中引入粒子群优化算法进行位置估计。利用PHAT(Phase Transform)加权函数的广义互相关法计算得到时延差的真实值,结合麦克风的坐标位置,通过几何关系计算出假设声源到达麦克风之间的时延差的估计值。设计时延真实值和估计值差值的平方和为粒子适应度函数,利用粒子群优化算法搜索空间中符合适应度函数的声源点,实现声源位置估计。仿真结果表明,在计算速度与球形插值法相近的情况下,文中所提算法比球形插值法具有更好的鲁棒性和抗噪性。
文摘Maximum likelihood estimation(MLE)is an effective method for localizing radioactive sources in a given area.However,it requires an exhaustive search for parameter estimation,which is time-consuming.In this study,heuristic techniques were employed to search for radiation source parameters that provide the maximum likelihood by using a network of sensors.Hence,the time consumption of MLE would be effectively reduced.First,the radiation source was detected using the k-sigma method.Subsequently,the MLE was applied for parameter estimation using the readings and positions of the detectors that have detected the radiation source.A comparative study was performed in which the estimation accuracy and time consump-tion of the MLE were evaluated for traditional methods and heuristic techniques.The traditional MLE was performed via a grid search method using fixed and multiple resolutions.Additionally,four commonly used heuristic algorithms were applied:the firefly algorithm(FFA),particle swarm optimization(PSO),ant colony optimization(ACO),and artificial bee colony(ABC).The experiment was conducted using real data collected by the Low Scatter Irradiator facility at the Savannah River National Laboratory as part of the Intelligent Radiation Sensing System program.The comparative study showed that the estimation time was 3.27 s using fixed resolution MLE and 0.59 s using multi-resolution MLE.The time consumption for the heuristic-based MLE was 0.75,0.03,0.02,and 0.059 s for FFA,PSO,ACO,and ABC,respectively.The location estimation error was approximately 0.4 m using either the grid search-based MLE or the heuristic-based MLE.Hence,heuristic-based MLE can provide comparable estimation accuracy through a less time-consuming process than traditional MLE.
