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
To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained ...To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained functions are combined to be an objective function.During the evolutionary process,the current optimal solution is found and treated as the reference point to divide the population into three sub-populations:one feasible and two infeasible ones.Different evolutionary operations of single or multi-objective optimization are respectively performed in each sub-population with elite strategy.Thirteen famous benchmark functions are selected to evaluate the performance of PEAES in comparison of other three optimization methods.The results show the proposed method is valid in efficiency,precision and probability for solving single-objective constrained optimization problems.展开更多
To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solve...To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱcould well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.展开更多
针对当前各类路径优化算法搜索规模较小、收敛速度较慢、全局搜索与局部搜索不平衡等问题,提出一种多策略融合的改进灰狼优化算法(multi-strategy fusion of grey wolf optimization algorithm,MGWO)。通过引入精英反向优化策略对种群...针对当前各类路径优化算法搜索规模较小、收敛速度较慢、全局搜索与局部搜索不平衡等问题,提出一种多策略融合的改进灰狼优化算法(multi-strategy fusion of grey wolf optimization algorithm,MGWO)。通过引入精英反向优化策略对种群进行初始化,提高初始解的质量。采用自适应权重机制,动态调整最优狼的领导能力。通过分段搜索方法,提升平衡局部搜索与全局探索的能力。仿真实验结果表明:该算法表现出色,能快速找到最优路径,提高算法的整体性能,具有一定借鉴作用。展开更多
为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite ...为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies,QGGTO)。融合二次插值策略和精英个体遗传策略,促进候选解之间的信息交流以加速收敛,并维持种群遗传多样性以避免局部最优。针对包含规则障碍物和不规则障碍物的复杂地形场景,构建了综合考虑行走距离、安全性和转向角度的成本函数,用于统一评估算法的路径规划性能。实验结果表明:QGGTO整体寻优性能优于GTO等7种竞争算法。在4种复杂障碍环境下,QGGTO能够辅助机器人规划出最接近全局最优的路径,验证了其在实际应用中的有效性。展开更多
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To solve single-objective constrained optimization problems,a new population-based evolutionary algorithm with elite strategy(PEAES) is proposed with the concept of single and multi-objective optimization.Constrained functions are combined to be an objective function.During the evolutionary process,the current optimal solution is found and treated as the reference point to divide the population into three sub-populations:one feasible and two infeasible ones.Different evolutionary operations of single or multi-objective optimization are respectively performed in each sub-population with elite strategy.Thirteen famous benchmark functions are selected to evaluate the performance of PEAES in comparison of other three optimization methods.The results show the proposed method is valid in efficiency,precision and probability for solving single-objective constrained optimization problems.
基金Supported by the National"Thirteenth Five-year Plan"National Key Program(2016YFD0701301)the Heilongjiang Provincial Achievement Transformation Fund Project(NB08B-011)。
文摘To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱcould well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.
文摘针对当前各类路径优化算法搜索规模较小、收敛速度较慢、全局搜索与局部搜索不平衡等问题,提出一种多策略融合的改进灰狼优化算法(multi-strategy fusion of grey wolf optimization algorithm,MGWO)。通过引入精英反向优化策略对种群进行初始化,提高初始解的质量。采用自适应权重机制,动态调整最优狼的领导能力。通过分段搜索方法,提升平衡局部搜索与全局探索的能力。仿真实验结果表明:该算法表现出色,能快速找到最优路径,提高算法的整体性能,具有一定借鉴作用。
文摘为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies,QGGTO)。融合二次插值策略和精英个体遗传策略,促进候选解之间的信息交流以加速收敛,并维持种群遗传多样性以避免局部最优。针对包含规则障碍物和不规则障碍物的复杂地形场景,构建了综合考虑行走距离、安全性和转向角度的成本函数,用于统一评估算法的路径规划性能。实验结果表明:QGGTO整体寻优性能优于GTO等7种竞争算法。在4种复杂障碍环境下,QGGTO能够辅助机器人规划出最接近全局最优的路径,验证了其在实际应用中的有效性。
