共查询到10条相似文献,搜索用时 140 毫秒
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A modified artificial bee colony algorithm 总被引:5,自引:0,他引:5
Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms. 相似文献
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Artificial bee colony algorithm (ABC) is a relatively new optimization algorithm. However, ABC does well in exploration but badly in exploitation. One possible way to improve the exploitation ability of the algorithm is to combine ABC with other operations. Differential evolution (DE) can be considered as a good choice for this purpose. Based on this consideration, we propose a new algorithm, i.e. DGABC, which combines DE with gbest-guided ABC (GABC) by an evaluation strategy with an attempt to utilize more prior information of the previous search experience to speed up the convergence. In addition, to improve the global convergence, when producing the initial population, a chaotic opposition-based population initialization method is employed. The comparison results on a set of 27 benchmark functions demonstrate that the proposed method has better performance than the other algorithms. 相似文献
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自适应Tent混沌搜索的人工蜂群算法 总被引:1,自引:0,他引:1
为了有效改善人工蜂群算法(artificial bee colony algorithm,ABC)的性能,结合Tent混沌优化算法,提出自适应Tent混沌搜索的人工蜂群算法.该算法使用Tent混沌以改善ABC的收敛性能,避免陷入局部最优解,首先应用Tent映射初始化种群,使得初始个体尽可能均匀分布,其次自适应调整混沌搜索空间,并以迄今为止搜索到的最优解产生Tent混沌序列,从而获得最优解.通过对6个复杂高维的基准函数寻优测试,仿真结果表明,该算法不仅加快了收敛速度,提高了寻优精度,与其他最近改进人工蜂群算法相比,其性能整体较优,尤其适合复杂的高维函数寻优. 相似文献
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Improved artificial bee colony algorithm for global optimization 总被引:7,自引:0,他引:7
The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms. 相似文献
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针对人工蜂群算法初始化群体分布不均匀和局部搜索能力弱的问题,本文提出了一种增强局部搜索能力的人工蜂群算法(ESABC)。首先,在种群初始化阶段采用高维洛伦兹混沌系统,得到遍历性好、有规律的初始群体,避免了随机初始化的盲目性。然后,采用基于对数函数的适应度评价方式,以增大种群个体间差异,减小选择压力,避免过早收敛。最后,在微分进化算法的启发下,提出了一种新的搜索策略,采用当前种群中的最佳个体来引导下一代的更新,以提高算法的局部搜索能力。通过对12个经典测试函数的仿真实验,并与其他经典的改进人工蜂群算法对比,结果表明:本文算法具有良好的寻优性能,无论在解的精度还是收敛速度方面效果都有所提高。 相似文献
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Zahra Beheshti 《控制论与系统》2013,44(7-8):452-474
AbstractMany meta-heuristic algorithms have been proposed to solve continuous optimization problems. Hence, researchers have applied various techniques to change these algorithms for discrete search spaces. Artificial bee colony (ABC) algorithm is one of the well-known algorithms for real search spaces. ABC has a good ability in exploration but it is weak in exploitation. Several binary versions of ABC have been proposed so far. Since the methods are based on the standard ABC, they have the disadvantage of ABC. In this article, a new binary ABC called binary multi-neighborhood ABC (BMNABC) has been introduced to enhance the exploration and exploitation abilities in the phases of ABC. BMNABC applies the near and far neighborhood information with a new probability function in the first and second phases. A more conscious search than the standard ABC is done in the third phase for those solutions which have been not improved in the previous phases. The performance of algorithm has been evaluated by low- and high-dimensional functions and the 0-1 multidimensional knapsack problems. The proposed method has been compared with state-of-the-art algorithms. The results showed that BMNABC had a better performance in terms of solution accuracy and convergence speed. 相似文献
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针对标准人工蜂群算法收敛速度慢和易陷入早熟收敛等问题,提出一种快速收敛人工蜂群算法。首先借助反向学习理论初始化种群来提高初始解的分布质量,并在雇佣蜂和跟随蜂阶段引入向量整体扰动搜索方程加快局部搜索;然后为了跳出局部最优,采用一种随机更新搜索策略来增加蜂群多样性以平衡全局探索和局部利用能力;最后通过八个标准测试函数的仿真实验,发现所提出的算法与几个改进的人工蜂群算法相比,具有更快的收敛速度且获得了更高的求解精度,验证了算法的优越性。 相似文献