人工蜂群算法在带约束圆形布局问题中的应用

人工蜂群算法是一种基于蜜蜂采蜜机制的新型演化算法。给出了带平衡约束的圆形布局问题的数学模型,介绍了人工蜂群算法的基本过程以及计算流程,将人工蜂群算法应用于带平衡约束的圆形布局优化中。通过两个实例进行仿真计算,并将计算结果与文献结果比较,验证了人工蜂群算法是解决此类问题的一种有效且实用的群智能算法。     

解决复杂优化问题的一个有效工具——蜂群优化算法*

蜂群的某些群智能行为在昆虫中是很独特的,因此来源于蜂群智能行为的各种优化算法在解决某些实际问题时是很有效的。较之其他的优化算法,目前国内关于蜂群优化算法的文献相对较少。简要介绍了若干蜂群优化算法的发展概况,并探讨了一些未来可做的工作。     

Artificial bee colony algorithm and pattern search hybridized for global optimization

Artificial bee colony algorithm is one of the most recently proposed swarm intelligence based optimization algorithm. A memetic algorithm which combines Hooke–Jeeves pattern search with artificial bee colony algorithm is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the exploration phase realized by artificial bee colony algorithm and the exploitation phase completed by pattern search. The proposed algorithm was tested on a comprehensive set of benchmark functions, encompassing a wide range of dimensionality. Results show that the new algorithm is promising in terms of convergence speed, solution accuracy and success rate. The performance of artificial bee colony algorithm is much improved by introducing a pattern search method, especially in handling functions having narrow curving valley, functions with high eccentric ellipse and some complex multimodal functions.     

Artificial bee colony algorithm with gene recombination for numerical function optimization

Artificial bee colony (ABC) algorithm is a stochastic and population-based optimization method, which mimics the collaborative foraging behaviour of honey bees and has shown great potential to handle various kinds of optimization problems. However, ABC often suffers from slow convergence speed since its internal mechanism and solution search equation do well in exploration, but badly in exploitation. In order to solve this knotty issue, inspired by the natural phenomenon that the good individuals (solutions) always contain good genes (variables) and the effective combination of the superior genes from different good individuals could more easily produce better offspring, we introduce a novel gene recombination operator (GRO) into ABC to accelerate convergence. To be specific, in GRO, a part of good solutions in the current population are selected to produce candidate solutions by the gene combination. Especially, each good solution recombines with only one other good solution to generate only one candidate solution. In addition, GRO will be launched at the end of each generation. In order to validate its efficiency and effectiveness, GRO is embedded into nine versions of ABC, i.e., the original ABC, GABC, best-so-far ABC(BSFABC), MABC, CABC, ABCVSS, qABC, dABC and distABC, while yields GRABC, GRGABC, GRBSFABC, GRMABC, GRCABC, GRABCVSS, GRqABC, GRdABC and GRdistABC respectively. The experimental results on 22 benchmark functions demonstrate that GRO could enhance the exploitation ability of ABCs and accelerate convergence without loss of diversity.     

矩形布局优化问题的局部搜索蜂群算法

带平衡约束的矩形布局问题属于组合优化问题,当问题规模增大时求解困难。为提高求解效率,设计了一个蜂群算法,通过分析解的分布,提供了基于贪心策略的群体初始化方案,选择了有效的变异算子,将蜂群算法的搜索空间聚焦于最优解可能的区域。另外设计了一个二次局部搜索算法,对解的质量进行进一步提升。在10个公开的案例上与目前性能最好的算法进行了对照,提出的蜂群算法在其中9个较大规模的案例上超过了现有算法。理论分析和实验结果表明,相对于现有算法,所提蜂群算法能明显提高求解效率。     

Artificial bee colony algorithm with strategy and parameter adaptation for global optimization

The artificial bee colony (ABC) algorithm has been successfully applied to solve a wide range of real-world optimization problems. However, the success of ABC in solving a specific problem crucially depends on appropriately choosing the foraging strategies and its associated parameters. In this paper, we propose a strategy and parameter self-adaptive selection ABC algorithm (SPaABC), in which both employed bees search strategies and their associated control parameter values are gradually self-adaptive by learning from their previous experiences in generating promising solutions. In order to verify the performance of our approach, SPaABC algorithm is compared to many recently related algorithms on eighteen benchmark functions. Experimental results indicate that the proposed algorithm achieves competitive performance on most test instances.

     

量子粒子群优化的人工蜂群算法

针对传统的人工蜂群算法在求解函数优化问题中具有收敛速度慢、局部搜索能力低的缺点,将量子粒子群优化算法中粒子位移的更新方法引入到跟随蜂的局部搜索策略中,使人工蜂群具有更高的局部搜索能力.6个标准测试函数的仿真实验结果表明:与传统的人工蜂群算法相比,改进后的人工蜂群算法在收敛速度和寻优精度上大幅提高.     

求解工程约束优化问题的PSO-ABC混合算法*

针对包含约束条件的工程优化问题,提出了基于人工蜂群的粒子群优化PSO-ABC算法。将PSO中较优的粒子作为ABC算法的蜜源,并使用禁忌表存储其局部极值,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性规则进行约束处理,将粒子种群分为可行子群和不可行子群,并在ABC算法产生蜜源的过程中保留部分较优的可行解和不可行解的信息,弥补了可行性规则处理最优点位于约束边界附近的问题时存在的不足。四个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。     

基于人工蜂群的连续域蚁群优化算法

连续域蚁群优化算法是蚁群优化算法的一个重要研究方向,针对连续域蚁群优化算法(ACOR)计算时间较长、易陷入局部最优的问题,提出了一种基于人工蜂群的连续域蚁群优化算法(ABCACOR)。首先,引入一种替代机制来选择指导解,以替换原来的基于排序的选择方式,目的是节约计算时间和尽可能地保持搜索的多样性;其次,结合人工蜂群算法的搜索策略来提高算法的全局搜索能力,进一步减少计算时间和提高求解精度。通过对大量的测试函数进行仿真实验,结果表明,ABC-ACOR算法较现有的一些连续域蚁群算法具有更好的寻优能力。     

Artificial bee colony algorithm with memory

Artificial bee colony algorithm (ABC) is a new type of swarm intelligence methods which imitates the foraging behavior of honeybees. Due to its simple implementation with very small number of control parameters, many efforts have been done to explore ABC research in both algorithms and applications. In this paper, a new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior. The memory mechanism is applied to guide the further foraging of the artificial bees. Essentially, ABCM is inspired by the biological study of natural honeybees, rather than most of the other ABC variants that integrate existing algorithms into ABC framework. The superiority of ABCM is analyzed on a set of benchmark problems in comparison with ABC, quick ABC and several state-of-the-art algorithms.     

受粒子群和差分进化启发的人工蜂群算法

针对基本人工蜂群算法搜索策略探索能力强而开发能力弱的特点,受粒子群和差分进化思想的启发,提出了两种新的搜索策略：PSO-DE-PABC和PSO-DE-GABC。前者在随机个体附近产生新的候选位置以提高算法的多样性;后者在最优解附近产生新的候选位置以提高算法的收敛速度,并加入差分进化中的差异向量来增加种群的多样性。在此基础上,引入维度因子来控制算法的收敛速度,并且使用一种利用当前种群信息的侦查策略来增强算法的局部搜索能力。通过对10组标准测试函数的实验仿真并与基本ABC、GABC和ABC/best算法相比,结果表明PSO-DE-GABC和PSO-DE-PABC对数值优化具有更高的收敛速度和收敛精度。     

分布式人工蜂群免疫算法求解函数优化问题

为了克服人工蜂群算法由于开发能力较弱而导致收敛速度慢、搜索精度不高等缺点,结合子蜂群思想和免疫克隆选择算法,提出一种基于分布式精英进化模型的人工蜂群免疫算法。首先对外层子蜂群进行启发式快速人工蜂群操作以提高收敛速度;然后对内层精英蜂群进行免疫克隆选择操作,进一步提高了算法的收敛精度和全局搜索能力。仿真结果表明了该算法在求解函数优化问题上的有效性和优越性。     

多选择背包问题的人工蜂群算法*

多选择背包问题是组合优化中的NP难题之一,采用一种新的智能优化算法——人工蜂群算法进行求解。该算法通过雇佣蜂、跟随蜂和侦察蜂的局部寻优来实现全局最优。基于算法实现的核心思想,用MATLAB编程实现,对参考文献的算例进行仿真测试。与其他算法进行了比较,获得了满意的结果。这说明了算法在解决该问题上的可行性与有效性,拓展了人工蜂群算法的应用领域。     

求解连续空间优化问题的改进蜂群算法

为了有效地解决人工蜂群算法容易陷入局部最优的缺陷,提出了一种改进蜂群算法。首先,利用反向学习方法构建初始种群,以提高初始化解的质量。同时,利用分布估计算法构造优秀个体解空间的概率模型以进行邻域搜索,以改善算法的搜索性能并防止陷入局部最优。对连续空间优化问题进行了仿真实验,结果表明改进算法具有较快的收敛速度,全局寻优能力显著提高。     

关于矩阵乘法问题的人工蜂群优化算法研究

矩阵乘法运算作为计算机科学和数学的一个基本运算,在科学研究和工程计算中有着广泛的应用。确定2个矩阵乘积所需要的最小乘法数是当今计算机代数中一直未能求解的重要问题之一。通过将矩阵乘法问题建模为一个组合优化问题,采用人工蜂群启发式搜索算法进行矩阵乘法问题求解。对人工蜂群算法进行了改进,给出一种绕圈遍历方法,避免了对同一个解的相同邻域的重复搜索。通过在2×2矩阵乘法问题上的数值实验验证了算法的有效性,所提算法能够快速地找到2×2矩阵分解的乘积方法。     

Hybrid bee colony optimization for examination timetabling problems

Swarm intelligence is a branch of artificial intelligence that focuses on the actions of agents in self-organized systems. Researchers have proposed a bee colony optimization (BCO) algorithm as part of swarm intelligence. BCO is a meta-heuristic algorithm based on the foraging behavior of bees. This study presents a hybrid BCO algorithm for examination timetabling problems. Bees in the BCO algorithm perform two main actions: forward pass and backward pass. Each bee explores the search space in forward pass and then shares information with other bees in the hive in backward pass. This study found that a bee decides to be either a recruiter that searches for a food source or a follower that selects a recruiter bee to follow on the basis of roulette wheel selection. In forward pass, BCO is supported along with other local searches, including the Late Acceptance Hill Climbing and Simulated Annealing algorithms. We introduce three selection strategies (tournament, rank and disruptive selection strategies) for the follower bees to select a recruiter to maintain population diversity in backward pass. The disruptive selection strategy outperforms tournament and rank selections. We also introduce a self-adaptive mechanism to select a neighborhood structure to enhance the neighborhood search. The proposed algorithm is evaluated against the latest methodologies in the literature with respect to two standard examination timetabling problems, namely, uncapacitated and competition datasets. We demonstrate that the proposed algorithm produces one new best result on uncapacitated datasets and comparable results on competition datasets.     

具有混沌搜索策略的蜂群优化算法

提出一种改进人工蜂群局部搜索能力的优化算法,对陷入局部最优值的雇佣蜂,使用禁忌表存储其局部极值,并引入混沌序列重新初始化,在迭代中产生局部极值的邻域点,帮助其逃离束缚并快速搜寻到最优解.改进算法有效地结合标准蜂群算法的全局优化能力、禁忌表的记忆能力和混沌局部搜索能力,对经典函数的测试计算表明,改进算法提高r蜂群寻优能力,在收敛速度和精度上均优于标准蜂群算法,适合工程应用中的复杂函数优化问题.     

Improved artificial bee colony algorithm for global optimization

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.