共查询到19条相似文献,搜索用时 947 毫秒
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通过算法混合提出了一种改进混沌粒子群优化算法。将混沌搜索融入到粒子群优化算法中,建立了早熟收敛判断和处理机制,显著提高了优化算法的局部搜索效率和全局搜索性能。将改进混沌粒子群优化算法应用于聚丙烯生产调优中,首先建立了聚丙烯最优牌号切换模型,然后采用改进混沌粒子群优化算法求解该最优牌号切换模型。优化结果:表明,与常规混沌粒子群优化算法相比,改进混沌粒子群优化算法具有更佳的优化效率和全局性能。 相似文献
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针对时变输入/输出过程神经网络的训练问题,提出一种基于混沌遗传与带有动态惯性因子的粒子群优化相结合的学习方法。综合利用粒子群算法的经验记忆、信息共享和混沌遗传算法的混沌轨道遍历搜索性质,基于PNN训练目标函数,构建两种算法相混合的进化寻优机制,通过适应度评估和优化效率分析自适应调节混沌遗传与粒子群算法的切换,实现网络参数在可行解空间的全局优化求解。实验结果表明,该算法较大提高了PNN的训练效率。 相似文献
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一种基于混沌优化的混合粒子群算法 总被引:1,自引:1,他引:0
粒子群算法是一类基于群智能的优化搜索算法。该算法初期收敛很快,但后期易陷入局部最优点。为了提高粒子群算法的性能,将粒子群算法全局搜索的快速性和混沌算法的一定范围内的遍历性二者结合,提出一种基于混沌优化的混合粒子群算法。该算法首先用粒子群算法进行快速搜索,当出现早熟收敛时,对局部较优的部分粒子和全局极值采用混沌优化策略。对两个典型的测试函数进行仿真表明,该算法能够摆脱局部极值,得到全局最优。将其用于(N+M)系统费用模型求解,得到最优解,同样验证了该算法搜索效率、精度优于一般的粒子群算法,同时具有较好的收敛稳定性。 相似文献
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针对非线性极大极小问题目标函数不可微的特点,提出了一种混沌万有引力搜索算法的求解方法。该算法采用基于万有引力定律的优化机制引导群体进行全局探索,并基于混沌运动的随机性、遍历性和规律性特点,利用混沌优化对当前最优位置进行精细搜索,有效抑制算法早熟收敛现象,提高优化性能。数值实验结果表明,该算法具有计算精度高、数值稳定性好等特点。 相似文献
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为提高组搜索优化(GSO)算法的性能,结合混沌方法的全局搜索特性,提出一种新的基于混沌搜索的组搜索优化(CGSO)算法。此方法中,生产者利用混沌搜索方法不断寻找较好的位置;占领者结合当前生产者的位置和自己运动到目前为止的最好位置对自己当前的位置进行更新;徘徊者采用混沌变异方法探索新的位置。该算法运用Logistic映射的初值敏感性扩大搜索范围,利用其全局遍历性进行位置搜索,有效地提高了算法的全局收敛性。采用CGSO、GSO算法对四个典型的函数优化问题进行了仿真实验,仿真结果验证了方法的有效性。 相似文献
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Yousef Hosseinzadeh Nasser Taghizadieh Shahin Jalili 《Neural computing & applications》2016,27(4):953-971
The optimal design of a truss structure with dynamic frequency constraints is a highly nonlinear optimization problem with several local optimums in its search space. In this type of structural optimization problems, the optimization methods should have a high capability to escape from the traps of the local optimums in the search space. This paper presents hybrid electromagnetism-like mechanism algorithm and migration strategy (EM–MS) for layout and size optimization of truss structures with multiple frequency constraints. The electromagnetism-like mechanism (EM) algorithm simulates the attraction and repulsion mechanism between the charged particles in the field of the electromagnetism to find optimal solutions, in which each particle is a solution candidate for the optimization problem. In the proposed EM–MS algorithm, two mechanisms are utilized to update the position of particles: modified EM algorithm and a new migration strategy. The modified EM algorithm is proposed to effectively guide the particles toward the region of the global optimum in the search space, and a new migration strategy is used to provide efficient exploitation between the particles. In order to test the performance of the proposed algorithm, this study utilizes five benchmark truss design examples with frequency constraints. The numerical results show that the EM–MS algorithm is an alternative and competitive optimizer for size and layout optimization of truss structures with frequency constraints. 相似文献
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Ching-Hung Lee Fu-Kai Chang Che-Ting Kuo Hao-Hang Chang 《International journal of systems science》2013,44(2):231-247
This article introduces a novel hybrid evolutionary algorithm for recurrent fuzzy neural systems design in applications of nonlinear systems. The hybrid learning algorithm, IEMBP-improved electromagnetism-like (EM) with back-propagation (BP) technique, combines the advantages of EM and BP algorithms which provides high-speed convergence, higher accuracy and less computational complexity (computation time in seconds). In addition, the IEMBP needs only a small population to outperform the standard EM that uses a larger population. For a recurrent neural fuzzy system, IEMBP simulates the ‘attraction’ and ‘repulsion’ of charged particles by considering each neural system parameters as a charged particle. The EM algorithm is modified in such a way that the competition selection is adopted and the random neighbourhood local search is replaced by BP without evaluations. Thus, the IEMBP algorithm combines the advantages of multi-point search, global optimisation and faster convergence. Finally, several illustration examples for nonlinear systems are shown to demonstrate the performance and effectiveness of IEMBP. 相似文献
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基于遗传算法混沌系统同步的研究 总被引:7,自引:1,他引:7
把混沌同步和混沌控制相结合, 利用引导混沌轨道的基本原理, 将模拟自然界生物进化过程的遗传算法用于混沌同步, 提出基于遗传算法引导混沌轨道, 从而实现混沌系统同步的新方法, 目的是使初始条件不同的混沌系统在小扰动作用下能迅速到达同步, 并采取策略使同步得以维持. 以H啨nonMap系统为例的仿真表明, 用此方法实现同步效果良好. 相似文献
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Zhang Zhisheng 《人工智能实验与理论杂志》2013,25(4):493-502
Chaotic electromagnetism-like mechanism algorithm (CEMA) is first proposed in this paper, which is the integration of electromagnetism-like mechanism algorithm (EMA) and chaos theory. EMA simulates the attraction and repulsion mechanism for particles in the electromagnetic field. Every solution is a charged particle, and it moves to optimum solution according to certain criteria which need several steps. To enrich the searching behaviour and to avoid being trapped into local optimum, chaotic dynamics is incorporated into EMA. CEMA possesses excellent global optimal performance, simple programming realisation and good convergence, and it is used in economic load dispatch of power systems. Through performance comparison, it is obvious that the solution is superior to other optimisation algorithms. It can be applied to other research problems in power systems. 相似文献
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In this paper, a hybrid of algorithms for electromagnetism-like mechanisms (EM) and particle swarm optimisation (PSO), called HEMPSO, is proposed for use in designing a functional-link-based Petri recurrent fuzzy neural system (FLPRFNS) for nonlinear system control. The FLPRFNS has a functional link-based orthogonal basis function fuzzy consequent and a Petri layer to eliminate the redundant fuzzy rule for each input calculation. In addition, the FLPRFNS is trained by the proposed hybrid algorithm. The main innovation is that the random-neighbourhood local search is replaced by a PSO algorithm with an instant-update strategy for particle information. Each particle updates its information instantaneously and in this way receives the best current information. Thus, HEMPSO combines the advantages of multiple-agent-based searching, global optimisation, and rapid convergence. Simulation results confirm that HEMPSO can be used to perform global optimisation and offers the advantage of rapid convergence; they also indicate that the FLPRFNS exhibits high accuracy. 相似文献
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针对类电磁机制算法存在局部搜索能力差的问题,提出一种基于单纯形法的混合类电磁机制算法。该混合算法首先利用反向学习策略构造初始种群以保证粒子均匀分布在搜索空间中。利用单纯形法对最优粒子进行局部搜索,增强了算法在最优点附近的局部搜索能力,以加快算法的收敛速度。四个基准测试函数的仿真实验结果表明,该算法具有更好的寻优性能。 相似文献
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基于混合量子进化计算的混沌系统参数估计 总被引:1,自引:0,他引:1
混沌系统参数估计本质上是一多维参数优化问题.为精确估计混沌系统的未知参数,本文提出一种混合量子进化算法(HQEA)用于求解该优化问题,该方法采用实数量子角形式表示染色体,用量子比特的概率作为个体的当前位置信息;提出由差分进化计算更新量子位置状态的量子差分进化算法(QDE),并将其与实数编码量子进化算法(RQEA)相融合,以便令算法在解空间的全局探索和局部开发能力之间取得平衡.算法还引入量子非门算子,对当前最佳个体中按某个概率选中的量子比特位,进行变换操作,以便增强算法跳出局部最优解的能力.基准函数测试表明混合算法的全局搜索能力及可靠性都有很大改善.通过Lorenz混沌系统进行数值仿真,结果表明了该混合算法的有效性. 相似文献
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Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some modifications are made for improving the performance of EM algorithm. The process of calculating the total force is simplified and an improved total force formula is adopted to accelerate the searching for optimal solution. In order to improve the accuracy of EM algorithm, a parameter called as move probability is introduced into the move formula where an elitist strategy is also adopted. And then, to handle the constraints, the feasibility and dominance rules are introduced and the corresponding charge formula is used for biasing feasible solutions over infeasible ones. Finally, 13 classical functions, three engineering design problems and 22 benchmark functions in CEC’06 are tested to illustrate the performance of proposed algorithm. Numerical results show that, compared with other versions of EM algorithm and other state-of-art algorithms, the improved EM algorithm has the advantage of higher accuracy and efficiency for constrained optimization problems. 相似文献