共查询到20条相似文献，搜索用时 218 毫秒
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针对传统的蚁群算法设计机器人避障路径规划,自适应能力差,全局优化能力和搜索速度不好的问题,在传统算法的基础上,提出一种采用奖惩规则格栅建模的机器人避障规划算法。提出构建模型主体的行为规则和避障规则,通过在栅格环境中设置量子遗传进化的多个有效的行为规则,设计了信息素更新的奖惩规则,修改其路径上的信息素,改变量子本身携带的信息素,得到优化避障最小距离。最终获得了复杂环境下的最优路径。仿真实验表明采用该算法进行机器人避障路径规划,在未知复杂环境下能够快速地规划出安全的优化路径,机器人避障路径规划具有很好的自适应性,相比传统的蚁群算法,其全局优化能力和搜索速度都得到了显著提高。 相似文献
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针对传统方法无法有效解决物流机器人一次访问若干点的全局路径规划问题。为此,提出一种基于栅格图法的移动物流机器人全局路径规划方法。通过栅格图法构造容易被移动物流机器人理解的仓储环境。在不考虑点和点间准确路径的情况下,按照移动物流机器人初始点是否处于出口,把全局路径规划问题划分成典型的TSP问题和TS-TSP问题,针对典型的TSP问题,将全局路径点看作种群个体,针对TS-TSP问题,将中间节点看作种群个体,以此构建移动物流机器人全局路径规划数学模型,并通过势场蚁群法对其进行求解,获取全局路径点的最优访问顺序,在此基础上,通过A*法计算准确的移动物流机器人全局路径规划结果。实验结果表明,采用所提方法收敛速度快,可快速得到全局最优解,且全局路径规划结果所需时间少,实用性强。 相似文献
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《科技通报》2015,(9)
为优化模糊神经网络的实时性、学习速度、收敛性、稳定性,在移动机器人局部路径规划中构建了基于实际隶属函数T-S(Takagi-Sugeno)模型的改进型模糊神经网络。对外部环境信息用多传感器(超声波、摄像头)采集并优化,将机器人横纵坐标及行进方向作为输入、机器人下一步行进方向及速度作为输出,以便机器人实现局部路径规划;结合动态环境下机器人路径规划的实际,综合考虑二维直角坐标体系下机器人、障碍物的位置、速度及运动方向等实时信息,推导出一种新的具有实际含义的隶属函数作为避碰隶属函数,并通过对比隐含层节点数对网络相对误差的影响来确定隶属函数层节点数,构建五层T-S型模糊神经网络;在此基础上应用改进型误差反传学习算法,通过matlab模拟实验仿真验证及对比分析,表明了改进型网络在优化网络实时性、学习速度、收敛性、稳定性方面有良好的性能。 相似文献
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提出一种基于多叉树Pareto最优解集的火灾扑救路径规划算法,对火灾现场的环境地图和火灾演化态势进行重构,实现对路径的优选,采用Pareto最优解集,构建基于多叉树Pareto最优解集的火源动态发展态势下的火灾扑救路径规划模型。实验结果表明,该模型能快速实现对火源热点的识别,并且规划路径能有效规避复杂建筑障碍物的干扰,实现对火灾扑救路径的最优选择。在动态未知环境中,对火灾扑救路径的规划和选择能达到最优,路径最短,分段较少,能有效地避免复杂建筑物的阻挡,有效节省了火灾扑救时间。 相似文献
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A direct approach to path planning of a 2-DOFs (Degrees of Freedom) spherical robot based on Bellman?s dynamic programming (DP) is introduced. The robot moves in an environment with obstacles and employs DP to find optimal trajectory by minimizing energy and preventing obstacle collision. While other path planning schemes rely on pre-planned optimal trajectories and/or feedback control techniques, in this approach there is no need to design a control system because DP yields the optimal control inputs in a closed loop (feedback) form. In other words, after completing the DP table, the optimal control inputs are known for every state in the admissible region and the robot can move toward the final position without colliding with obstacles. This enables the robot to function well in semi- or even non-observable environments. Results from several simulated experiments show that the proposed approach is capable of finding an optimal path from any given position/orientation towards a predefined target in an environment with obstacles within the admissible region. The method is very promising compared to other path planning schemes. 相似文献
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《Journal of The Franklin Institute》2023,360(6):4378-4398
Dynamic path planning for mobile robots is an urgent issue that needs to be solved because of the growing use of mobile robots in daily life and industrial operations. This work focuses on avoiding moving obstacles in dynamic situations. The computational effort required by some current algorithms makes them difficult to utilize for path planning in dynamic situations whilst the computational effort required by other methods makes them simple yet prone to local minima. In this paper, an improved simulated annealing (SA) algorithm for dynamic path planning is proposed. To reduce its computational effort, the initial path selection method and deletion operation are introduced. Simulation results show the improved SA algorithm outperforms other algorithms and provides optimal solutions in static and dynamic environments. 相似文献
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粒子群算法网络异常检测技术研究 总被引：1，自引：0，他引：1
提出了一种新的基于粒子群算法入侵检测方法模型。算法采用粒子群优化算法,有效地降低网络拓扑路径长度,通过优化算法来寻找聚类的中心。实验结果表明,提出的改进算法与传统的入侵检测算法相比,具有更好的入侵识别率和检测率。 相似文献
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常规粒子群算法(SPSO)在优化过程中易陷入局部最优,本文分析了常规粒子群算法陷入局部最优的原因,提出采用一种自适应粒子群算法(APSO)避免陷入局部最优,改善算法的收敛性和精度。最后用自适应粒子群算法设计宽带阶梯阻抗变换器,结果表明,与常规粒子群算法相比,自适应粒子群算法全局速度快、成功率和精度也有显著提高。 相似文献
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《Information processing & management》2022,59(5):103005
In real-life applications, resources in construction projects are always limited. It is of great practical importance to shorten the project duration by using intelligent models (i.e., evolutionary computations such as genetic algorithm (GA) and particle swarm optimization (PSO) to make the construction process reasonable considering the limited resources. However, in the general EC-based model, for example, PSO easily falls into a local optimum when solving the problem of limited resources and the shortest period in scheduling a large network. This paper proposes two PSO-based models, which are resource-constrained adaptive particle swarm optimization (RC-APSO) and an input-adaptive particle swarm optimization (iRC-APSO) to respectively solve the static and dynamic situations of resource-constraint problems. The RC-APSO uses adaptive heuristic particle swarm optimization (AHPSO) to solve the limited resource and shortest duration problem based on the analysis of the constraints of process resources, time limits, and logic. The iRC-APSO method is a combination of AHPSO and network scheduling and is used to solve the proposed dynamic resource minimum duration problem model. From the experimental results, the probability of obtaining the shortest duration of the RC-APSO is higher than that of the genetic PSO and GA models, and the accuracy and stability of the algorithm are significantly improved compared with the other two algorithms, providing a new method for solving the resource-constrained shortest duration problem. In addition, the computational results show that iRC-APSO can obtain the shortest time constraint and the design scheme after each delay, which is more valuable than the static problem for practical project planning. 相似文献
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We consider the problem of placing copies of objects in a distributed web server system to minimize the cost of serving read and write requests when the web servers have limited storage capacities. We formulate the problem as a 0–1 optimization problem and present a hybrid particle swarm optimization algorithm to solve it. The proposed hybrid algorithm makes use of the strong global search ability of particle swarm optimization (PSO) and the strong local search ability of tabu search to obtain high quality solutions. The effectiveness of the proposed algorithm is demonstrated by comparing it with the genetic algorithm (GA), simple PSO, tabu search, and random placement algorithm on a variety of test cases. The simulation results indicate that the proposed hybrid approach outperforms the GA, simple PSO, and tabu search. 相似文献
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本文针对标准人工蜂群算法开发能力较弱的缺点,借鉴粒子群算法的思想,将全局最优解引入,与引领蜂进行交叉操作,使蜂群进行有引导的探索,通过基准函数的测试,证明了改进后的算法性能有所提高。 相似文献
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《Journal of The Franklin Institute》2022,359(2):859-869
In this paper, we considered a time-optimal control problem for a new type of linear parameter varying (LPV) system which is obtained through data identification in the process of dealing with actual problems. The addition of non-linear terms is compensation for the method that does not require linear expansion at the equilibrium point. Since the objective function is the terminal time which is an implicit function concerning decision variables, it is a non-standard optimal control problem with uncertain terminal time. To find the global optimal solution to this problem, firstly, the control parameterization method is used to transform it into a nonlinear optimization problem of parameter selection, and then the modifed particle swarm optimization (PSO) algorithm is combined to solve the equivalent nonlinear programming problem. Numerical examples are used to illustrate the effectiveness of the proposed algorithm. 相似文献
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A new design method based on artificial bee colony algorithm for digital IIR filters 总被引：11，自引：0，他引：11
Nurhan Karaboga Author Vitae 《Journal of The Franklin Institute》2009,346(4):328-348
Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm. 相似文献