Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the ...Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.展开更多
Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a da...Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.展开更多
三维无人机路径规划问题旨在满足安全性条件的前提下为无人机规划出一条最佳的飞行路径.本文通过数学建模的方式构建出无人机路径规划的成本函数,从而无人机路径规划问题转化为多约束的优化问题,并使用元启发式算法来求解该问题.针对人...三维无人机路径规划问题旨在满足安全性条件的前提下为无人机规划出一条最佳的飞行路径.本文通过数学建模的方式构建出无人机路径规划的成本函数,从而无人机路径规划问题转化为多约束的优化问题,并使用元启发式算法来求解该问题.针对人工兔优化算法收敛慢以及易陷入局部最优的缺陷,本文开发了一种基于Levy飞行、自适应柯西变异以及精英群遗传策略改进的人工兔优化算法(Artificial Rabbit Optimization algorithm based on Levy flight,adaptive Cauchy mutation,and elite population Genetic strategy,LCGARO).将LCGARO与6个经典和先进的元启发式算法在29个CEC2017测试函数和6个复杂度不同的三维无人机路径规划地形场景中进行多方面对比实验.对比实验结果证明,在CEC2017测试函数的对比实验中,本文提出的LCGARO算法在22个测试函数中具有更优的寻优精度.在无人机路径规划实验中,LCGARO算法在5个地形场景中能够规划出总成本函数值最小的飞行路径.展开更多
The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effe...The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effective temperature control is crucial to ensure fuel cell reliability and durability. This paper uses active disturbance rejection control in the thermal management system to maintain the operating temperature and the stack inlet and outlet temperature difference at the set value. First, key cooling system modules such as expansion tanks, coolant circulation pumps and radiators based on Simulink were built. Then, physical modeling and simulation of the fuel cell cooling system was carried out. In order to ensure the effectiveness of the control strategy and reduce the parameter tuning workload, an active disturbance rejection control parameter optimization method using an elite genetic algorithm was proposed. When the optimized control strategy responds to input disturbances, the maximum overshoot of the system is only 1.23% and can reach stability again in 30 s, so the fuel cell temperature can be controlled effectively. Simulation results show that the optimized control strategy can effectively control the stack temperature and coolant temperature difference under the influence of stepped charging current without interference or with interference, and has strong robustness and anti-interference capability.展开更多
基金This work was supported by UK EPSRC(No.EP/E060722/01)Broil FAPESP(Proc.04/04289-6).
文摘Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
基金Supported by the National Science Foundation for Post-doctoral Scientists of China(20090460216)the National Defense Fundamental Research Foundation of China(B222006060)
文摘Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.
文摘三维无人机路径规划问题旨在满足安全性条件的前提下为无人机规划出一条最佳的飞行路径.本文通过数学建模的方式构建出无人机路径规划的成本函数,从而无人机路径规划问题转化为多约束的优化问题,并使用元启发式算法来求解该问题.针对人工兔优化算法收敛慢以及易陷入局部最优的缺陷,本文开发了一种基于Levy飞行、自适应柯西变异以及精英群遗传策略改进的人工兔优化算法(Artificial Rabbit Optimization algorithm based on Levy flight,adaptive Cauchy mutation,and elite population Genetic strategy,LCGARO).将LCGARO与6个经典和先进的元启发式算法在29个CEC2017测试函数和6个复杂度不同的三维无人机路径规划地形场景中进行多方面对比实验.对比实验结果证明,在CEC2017测试函数的对比实验中,本文提出的LCGARO算法在22个测试函数中具有更优的寻优精度.在无人机路径规划实验中,LCGARO算法在5个地形场景中能够规划出总成本函数值最小的飞行路径.
文摘The performance of proton exchange membrane fuel cells is very sensitive to temperature. The electrochemical reaction results directly in temperature variations in the proton exchange membrane fuel cell. Ensuring effective temperature control is crucial to ensure fuel cell reliability and durability. This paper uses active disturbance rejection control in the thermal management system to maintain the operating temperature and the stack inlet and outlet temperature difference at the set value. First, key cooling system modules such as expansion tanks, coolant circulation pumps and radiators based on Simulink were built. Then, physical modeling and simulation of the fuel cell cooling system was carried out. In order to ensure the effectiveness of the control strategy and reduce the parameter tuning workload, an active disturbance rejection control parameter optimization method using an elite genetic algorithm was proposed. When the optimized control strategy responds to input disturbances, the maximum overshoot of the system is only 1.23% and can reach stability again in 30 s, so the fuel cell temperature can be controlled effectively. Simulation results show that the optimized control strategy can effectively control the stack temperature and coolant temperature difference under the influence of stepped charging current without interference or with interference, and has strong robustness and anti-interference capability.
