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1.
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.  相似文献
2.
A comparative study of Artificial Bee Colony algorithm   总被引:26,自引:0,他引:26  
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.  相似文献
3.
0-1背包问题的蜂群优化算法   总被引:3,自引:0,他引:3  
在项目决策与规划、资源分配、货物装载、预算控制等工作中,提出了0-1背包问题.0-1背包问题是组合优化中的典型NP难题,根据群集智能原理,给出一种基于蜂群寻优思想的新算法—蜂群算法,并针对0-1背包问题进行求解.经实验仿真并与蚁群算法计算结果作对比,验证了算法在0-1背包问题求解上的有效性和更快的收敛速度.  相似文献
4.
This paper proposes a novel variant of quantum-behaved particle swarm optimization (QPSO) algorithm with the local attractor point subject to a Gaussian probability distribution (GAQPSO). The local attractor point in QPSO plays an important in that determining the convergence behavior of an individual particle. As such, the mean value and standard deviation of the proposed Gaussian probability distribution in GAQPSO are carefully selected. The distributions and diversities of the local attractor points in GAQPSO and QPSO are evaluated and compared. For the purpose of comparison, two variants of the GAQPSO algorithm are proposed by using a mutation probability and other types of probability distribution. The GAQPSO has been comprehensively evaluated on the suite of CEC2005 benchmark functions, and the experimental results are compared with those of the PSO and QPSO algorithms based on different probability distributions. It is shown by the results that the GAQPSO algorithm is an effective approach that can improve the QPSO performance considerably, that is, the GAQPSO algorithm is less likely to be stuck in local optima and hence it can achieve better solutions in most cases.  相似文献
5.
In this paper, we first refine a recently proposed metaheuristic called “Marriage in Honey-Bees Optimization” (MBO) for solving combinatorial optimization problems with some modifications to formally show that MBO converges to the global optimum value. We then adapt MBO into an algorithm called “Honey-Bees Policy Iteration” (HBPI) for solving infinite horizon-discounted cost stochastic dynamic programming problems and show that HBPI also converges to the optimal value.  相似文献
6.
The Team Orienteering Problem (TOP) is a particular vehicle routing problem in which the aim is to maximize the profit gained from visiting customers without exceeding a travel cost/time limit. This paper proposes a new and fast evaluation process for TOP based on an interval graph model and a Particle Swarm Optimization inspired Algorithm (PSOiA) to solve the problem. Experiments conducted on the standard benchmark of TOP clearly show that our algorithm outperforms the existing solving methods. PSOiA reached a relative error of 0.0005% whereas the best known relative error in the literature is 0.0394%. Our algorithm detects all but one of the best known solutions. Moreover, a strict improvement was found for one instance of the benchmark and a new set of larger instances was introduced.  相似文献
7.
武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.  相似文献
8.
群集智能是指由简单agent之间的局部交互作用表现出来的全局智能行为.群体稳定地聚集是有效完成任务的前提.本文主要利用Lyapunov稳定性理论,研究了带有分布时滞的群集智能聚集模型稳定性.  相似文献
9.
Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.  相似文献
10.
分析将蚁群优化算法应用于预防性维修周期工程寻优问题时遇到的算法参数选择困难等问题,提出将粒子群优化算法和空间划分方法引入该过程以改进原蚁群算法的寻优规则和历程.建立混合粒子群和蚁群算法的群智能优化策略:PS_ACO(Particle Swarm and Ant Colony Optimization),并将其应用于混联系统预防性维修周期优化过程中,以解决由于蚁群算法中参数选择不当和随机产生维修周期解值带来的求解精度差、寻优效率低等问题.算法的寻优结果对比分析表明:该PS_ACO算法应用于预防性维修周期优化问题,在寻优效率及寻优精度上有部分改进,且可相对削弱算法参数选择对优化结果的影响.  相似文献
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