A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search...A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.展开更多
During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution qual...During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.展开更多
A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems a...A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.展开更多
A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorith...A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase I. Adjustment strategy of steplength and intensive searches in Phase II are employed. The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.展开更多
A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociol...A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.展开更多
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK ...A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.展开更多
The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. ...The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.展开更多
This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching ph...This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.展开更多
In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on th...In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.展开更多
为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorith...为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.展开更多
为提高现有作物生长模型单产模拟精度,明确土壤水分供需变化与产量之间的动态关系,该研究构建了水文—作物生长耦合模型SWAT-EPIC(soil and water assessment tool-environmental policy integrated climate)。基于参数敏感性优化的扩...为提高现有作物生长模型单产模拟精度,明确土壤水分供需变化与产量之间的动态关系,该研究构建了水文—作物生长耦合模型SWAT-EPIC(soil and water assessment tool-environmental policy integrated climate)。基于参数敏感性优化的扩展傅里叶幅度敏感性检验算法(extended fourier amplitude sensitivity test,E-FAST),在陕北黄土高原建立包含土壤碳动态评估的苹果单产评估体系。结果表明,优化后的模型在旱作苹果单产的模拟精度误差减少了38.98%,模拟RMSE=2.56%,RRMSE≈9.8%,具有良好的模拟性能。降水对果园浅层土壤水分补给具有直接作用,而深层水分持续消耗加剧干旱胁迫,成为限制苹果单产提升的关键因素。此外,农地改种果园后0~10m土壤有机碳储量提升了14.85%。本研究表明,通过整合水文过程与作物生长机制,SWAT-EPIC耦合模型能够更全面反演区域水分与产量的响应关系,为干旱区果园水分管理与可持续高产提供科学依据。展开更多
基金supported by the National Natural Science Foundation of China(60870004)
文摘A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘During the last decade, many variants of the original particle swarm optimization （PSO） algorithm have been proposed for global numerical optimization, hut they usually face many challenges such as low solution quality and slow convergence speed on multimodal function optimization. A composite particle swarm optimization （CPSO） for solving these difficulties is presented, in which a novel learning strategy plus an assisted search mechanism framework is used. Instead of simple learning strategy of the original PSO, the proposed CPSO combines one particle＇s historical best information and the global best information into one learning exemplar to guide the particle movement. The proposed learning strategy can reserve the original search information and lead to faster convergence speed. The proposed assisted search mechanism is designed to look for the global optimum. Search direction of particles can be greatly changed by this mechanism so that the algorithm has a large chance to escape from local optima. In order to make the assisted search mechanism more efficient and the algorithm more reliable, the executive probability of the assisted search mechanism is adjusted by the feedback of the improvement degree of optimal value after each iteration. According to the result of numerical experiments on multimodal benchmark functions such as Schwefel, Rastrigin, Ackley and Griewank both with and without coordinate rotation, the proposed CPSO offers faster convergence speed, higher quality solution and stronger robustness than other variants of PSO.
文摘A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.
文摘A modification of evolutionary programming or evolution strategies for ndimensional global optimization is proposed. Based on the ergodicity and inherentrandomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase I. Adjustment strategy of steplength and intensive searches in Phase II are employed. The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
基金Projects(51105157, 50875101) supported by the National Natural Science Foundation of ChinaProject(2009AA043301) supported by the National High Technology Research and Development Program of China
文摘A fast global convergence algorithm, small-world optimization (SWO), was designed to solve the global optimization problems, which was inspired from small-world theory and six degrees of separation principle in sociology. Firstly, the solution space was organized into a small-world network model based on social relationship network. Secondly, a simple search strategy was adopted to navigate into this network in order to realize the optimization. In SWO, the two operators for searching the short-range contacts and long-range contacts in small-world network were corresponding to the exploitation and exploration, which have been revealed as the common features in many intelligent algorithms. The proposed algorithm was validated via popular benchmark functions and engineering problems. And also the impacts of parameters were studied. The simulation results indicate that because of the small-world theory, it is suitable for heuristic methods to search targets efficiently in this constructed small-world network model. It is not easy for each test mail to fall into a local trap by shifting into two mapping spaces in order to accelerate the convergence speed. Compared with some classical algorithms, SWO is inherited with optimal features and outstanding in convergence speed. Thus, the algorithm can be considered as a good alternative to solve global optimization problems.
文摘A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
基金supported in part by the National Outstanding Youth Foundation of P.R.China (60525303)the National Natural Science Foundation of P.R.China(60404022,60604004)+2 种基金the Natural Science Foundation of Hebei Province (102160)the special projects in mathematics funded by the Natural Science Foundation of Hebei Province(07M005)the NS of Education Office in Hebei Province (2004123).
文摘The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point.
基金Supported by National Natural Science Foundation of P.R.China(50275150)National Research Foundation for the Doctoral Program of Higher Education of P.R.China(20040533035)
基金supported by the National Natural Science Foundation of China(61601505)
文摘This paper introduces an optimization algorithm, the hummingbirds optimization algorithm(HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization.
文摘In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.
文摘为设计高效稳定的演化算法,将方程求根的不动点迭代思想引入到优化领域,通过将演化算法的寻优过程看作为在迭代框架下方程不动点的逐步显示化过程,设计出一种基于数学模型的演化新算法,即不动点演化算法(fixed point evolution algorithm,FPEA).该算法的繁殖算子是由Aitken加速的不动点迭代模型导出的二次多项式,其整体框架继承传统演化算法(如差分演化算法)基于种群的迭代模式.试验结果表明:在基准函数集CEC2014、CEC2019上,本文算法的最优值平均排名在所有比较算法中排名第1;在4个工程约束设计问题上,FPEA与CSA、GPE等多个算法相比,能以较少的计算开销获得最高的求解精度.
