A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider int...A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.展开更多
Finding the suitable solution to optimization problems is a fundamental challenge in various sciences.Optimization algorithms are one of the effective stochastic methods in solving optimization problems.In this paper,...Finding the suitable solution to optimization problems is a fundamental challenge in various sciences.Optimization algorithms are one of the effective stochastic methods in solving optimization problems.In this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization problems.In the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search space.Then,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm implementation.SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types.The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm.In addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.展开更多
Aiming at the shortcoming that certain existing blocking-matching algorithms,such as full search,three-step search,and diamond search algorithms,usually can not keep a good balance between high accuracy and low comput...Aiming at the shortcoming that certain existing blocking-matching algorithms,such as full search,three-step search,and diamond search algorithms,usually can not keep a good balance between high accuracy and low computational complexity,a block-matching motion estimation algorithm based on two-step search is proposed in this paper.According to the fact that the gray values of adjacent pixels will not vary fast,the algorithm employs an interlaced search pattern in the search window to estimate the motion vector of the object-block.Simulation and actual experiments demonstrate that the proposed algorithm greatly outperforms the well-known three-step search and diamond search algorithms,no matter the motion vector is large or small.Compared with the full search algorithm,the proposed one achieves similar performance but requires much less computation,therefore,the algorithm is well qualified for real-time video image processing.展开更多
In this letter, an improved three-step search algorithm is presented, which uses both gray and chromatic information to boost the performance with random optimization and converge the motion vectors to global optima. ...In this letter, an improved three-step search algorithm is presented, which uses both gray and chromatic information to boost the performance with random optimization and converge the motion vectors to global optima. Experimental results show that this algorithm can efficiently improve the PSNR after motion compensation.展开更多
面向园区综合能源系统中供能方与用能方的角色互换,以及园区低碳经济运行的强约束,提出了一种考虑动态参数的阶梯型碳交易机制和需求响应的园区级综合能源系统主从博弈优化调度方法。首先,将园区级综合能源系统中能源运营商(energy syst...面向园区综合能源系统中供能方与用能方的角色互换,以及园区低碳经济运行的强约束,提出了一种考虑动态参数的阶梯型碳交易机制和需求响应的园区级综合能源系统主从博弈优化调度方法。首先,将园区级综合能源系统中能源运营商(energy system operator,ESO)设定为上层领导者、综合能源系统园区设定为下层跟随者,并且能源运营商以最大化自身效益为目标,通过制定与园区间的购售电价格、碳交易基价、价格增长幅度,引导下层园区优化;下层园区以最小化其运行成本为目标,对上层发布的价格信息做出反应,从而构建主从博弈模型。其次,充分考虑园区级综合能源系统的低碳经济运行约束,在博弈模型中引入考虑动态参数的阶梯型碳交易机制以限制二氧化碳排放量,并在园区侧引入需求响应。最后,利用水母搜索算法对上层发布的购售电价、碳交易基价、价格增长幅度进行优化,利用CPLEX优化下层园区设备出力、需求响应以及购售电计划。仿真结果证明了所提模型和方法的有效性。展开更多
The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching ste...The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application,the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.展开更多
文摘A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.
基金PT(corresponding author)and SH was supported by the Excellence project PrF UHK No.2202/2020-2022Long-term development plan of UHK for year 2021,University of Hradec Králové,Czech Republic,https://www.uhk.cz/en/faculty-of-science/about-faculty/officia l-board/internal-regulations-and-governing-acts/governing-acts/deans-decision/2020#grant-compe tition-of-fos-uhk-excellence-for-2020.
文摘Finding the suitable solution to optimization problems is a fundamental challenge in various sciences.Optimization algorithms are one of the effective stochastic methods in solving optimization problems.In this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization problems.In the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search space.Then,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm implementation.SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types.The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm.In addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.
基金supported by the Lab Open Fund of Beijing Microchemical Research Institute(P2008026EB)
文摘Aiming at the shortcoming that certain existing blocking-matching algorithms,such as full search,three-step search,and diamond search algorithms,usually can not keep a good balance between high accuracy and low computational complexity,a block-matching motion estimation algorithm based on two-step search is proposed in this paper.According to the fact that the gray values of adjacent pixels will not vary fast,the algorithm employs an interlaced search pattern in the search window to estimate the motion vector of the object-block.Simulation and actual experiments demonstrate that the proposed algorithm greatly outperforms the well-known three-step search and diamond search algorithms,no matter the motion vector is large or small.Compared with the full search algorithm,the proposed one achieves similar performance but requires much less computation,therefore,the algorithm is well qualified for real-time video image processing.
文摘In this letter, an improved three-step search algorithm is presented, which uses both gray and chromatic information to boost the performance with random optimization and converge the motion vectors to global optima. Experimental results show that this algorithm can efficiently improve the PSNR after motion compensation.
文摘面向园区综合能源系统中供能方与用能方的角色互换,以及园区低碳经济运行的强约束,提出了一种考虑动态参数的阶梯型碳交易机制和需求响应的园区级综合能源系统主从博弈优化调度方法。首先,将园区级综合能源系统中能源运营商(energy system operator,ESO)设定为上层领导者、综合能源系统园区设定为下层跟随者,并且能源运营商以最大化自身效益为目标,通过制定与园区间的购售电价格、碳交易基价、价格增长幅度,引导下层园区优化;下层园区以最小化其运行成本为目标,对上层发布的价格信息做出反应,从而构建主从博弈模型。其次,充分考虑园区级综合能源系统的低碳经济运行约束,在博弈模型中引入考虑动态参数的阶梯型碳交易机制以限制二氧化碳排放量,并在园区侧引入需求响应。最后,利用水母搜索算法对上层发布的购售电价、碳交易基价、价格增长幅度进行优化,利用CPLEX优化下层园区设备出力、需求响应以及购售电计划。仿真结果证明了所提模型和方法的有效性。
基金Supported by Academic Innovation Project of Beijing(201106149)
文摘The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application,the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.
