共查询到20条相似文献,搜索用时 93 毫秒
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本文提出了一种微粒群算法与模拟退火算法相结合的混合优化方法,该方法在群体进化的每一代中,首先通过微粒群算法的进化方法来控制微粒的飞行方向,然后利用模拟退火算法来拓展其搜索领域。这样既可以利用微粒群算法的收敛快速性,又可以利用模拟退火算法的全局收敛性。本文还证明了该混合优化方法依概率1收敛于全局最优解。仿
真结果表明,在搜索空间维数增大时,该方法的全局收敛性明显优于基本微粒群算法。 相似文献
真结果表明,在搜索空间维数增大时,该方法的全局收敛性明显优于基本微粒群算法。 相似文献
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黄力明 《数值计算与计算机应用》2008,29(2):119-125
混沌微粒群优化算法利用了粒子群优化算法收敛速度快和混沌运动所具有的随机性、遍历性和初值敏感性,将混沌状态引入到优化变量中,把混沌的遍历范围映射到优化变量的取值范围.在算法执行过程中对优秀个体混沌扰动,有利于跳出局部极值点,搜索到全局最优解.分别用微粒群优化算法和混沌微粒群优化算法求解函数优化问题,对算法的性能进行检验,检验结果显示:混沌微粒群优化算法搜索全局最优解的成功率和收敛速度都要优于微粒群优化算法.将混沌微粒群优化算法与阈值法相结合,在算法初始化阶段对粒子位置混沌初始化;在算法运行期间对优秀个体进行混沌扰动避免落入局部最优,较好地解决了传统的多阈值图像分割方法中运算量大的问题.实验结果表明,混沌微粒群优化算法用于阈值寻优减少了搜索时间,提高了收敛率. 相似文献
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该算法先利用Christos贪心算法将整个搜索区域进行自适应分区段,在每一区段内搜索出最优位置,然后将各区段的最优位置组成一新微粒群,继续搜索全局最优位置。而在每个区段中,又将模拟退火算法引入到粒子群优化(PSO)之中,通过Boltzmann机制选择每一区段中局部极值,使新算法在不同阶段兼顾对多样性和收敛速度的不同要求。与其他混合PSO算法相比,仿真实验表明,新算法具有较高的解精度,能较好地解决过早收敛问题。 相似文献
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针对粒子群优化算法(PSO)在求解高维复杂优化问题时存在搜索精度不高和易陷入局部最优解的缺陷,借鉴混合蛙跳算法(SFLA)的群体爬山思想,提出一种基于群体爬山策略的混合粒子群优化算法(CMCPSO),并证明了CMCPSO算法的全局收敛性。对四个典型高维连续优化函数的求解表明,该算法不仅保持了PSO算法的快速收敛能力,而且吸收了SFLA算法局部精细搜索和保持种群多样性的优点,具有良好的全局收敛性。 相似文献
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In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied
in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be
regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective
Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the
standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive
with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to
solve multiobjective optimization problems. 相似文献
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Feng Zou Lei Wang Xinhong Hei Debao Chen Bin Wang 《Engineering Applications of Artificial Intelligence》2013,26(4):1291-1300
Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPs). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms. 相似文献
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Ilhem Boussaïd Amitava Chatterjee Patrick Siarry Mohamed Ahmed-Nacer 《Computers & Operations Research》2012
Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints. 相似文献
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Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems. 相似文献
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Machine Learning - Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the... 相似文献
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Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient... 相似文献
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In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability. 相似文献
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Amilkar Puris Rafael Bello Daniel Molina Francisco Herrera 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(3):511-525
Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems
in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level
during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic
called “variable mesh optimization” (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This
mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to
guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared
with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive
algorithm. 相似文献