Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism

Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

Improved artificial bee colony algorithm with mutual learning

The recently invented artificial bee colony （ABC） al- gorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of find- ing a neighboring food source. This paper presents an improved ABC algorithm with mutual learning （MutualABC） that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.

Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

The artificial bee colony （ABC） algorithm is a sim- ple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing ex- ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability P,. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self- adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en- hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition- based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func- tions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

Artificial Bee Colony Algorithm-based Parameter Estimation of Fractional-order Chaotic System with Time Delay

It is an important issue to estimate parameters of fractional-order chaotic systems in nonlinear science, which has received increasing interest in recent years. In this paper, time delay and fractional order as well as system's parameters are concerned by treating the time delay and fractional order as additional parameters. The parameter estimation is converted into a multi-dimensional optimization problem. A new scheme based on artificial bee colony ABC algorithm is proposed to solve the optimization problem. Numerical experiments are performed on two typical time-delay fractional-order chaotic systems to verify the effectiveness of the proposed method. © 2014 Chinese Association of Automation.

Aeroengine Nonlinear Sliding Mode Control Based on Artificial Bee Colony Algorithm

For a class of aeroengine nonlinear systems,a novel nonlinear sliding mode controller(SMC)design method based on artificial bee colony(ABC)algorithm is proposed.In view of the strong nonlinearity and uncertainty of aeroengines,sliding mode control strategy is adopted to design controller for the aeroengine.On basis of exact linearization approach,the nonlinear sliding mode controller is obtained conveniently.By using ABC algorithm,the parameters in the designed controller can be tuned to achieve optimal performance,resulting in a closedloop system with satisfactory dynamic performance and high steady accuracy.Simulation on an aeroengine verifies the effectiveness of the presented method.

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.

S-box:six-dimensional compound hyperchaotic map and artificial bee colony algorithm

Being as unique nonlinear components of block ciphers,substitution boxes（S-boxes） directly affect the security of the cryptographic systems.It is important and difficult to design cryptographically strong S-boxes that simultaneously meet with multiple cryptographic criteria such as bijection,non-linearity,strict avalanche criterion（SAC）,bits independence criterion（BIC）,differential probability（DP） and linear probability（LP）.To deal with this problem,a chaotic S-box based on the artificial bee colony algorithm（CSABC） is designed.It uses the S-boxes generated by the six-dimensional compound hyperchaotic map as the initial individuals and employs ABC to improve their performance.In addition,it considers the nonlinearity and differential uniformity as the fitness functions.A series of experiments have been conducted to compare multiple cryptographic criteria of this algorithm with other algorithms.Simulation results show that the new algorithm has cryptographically strong S-box while meeting multiple cryptographic criteria.

基于ABC-BP神经网络的光纤传感器光强补偿

为了减少光纤传感器测量过程中接收光强受到非线性因素的影响,提出利用人工蜂群算法(artificial bee colony, ABC)优化反向传播神经网络(BPNN)进行光强补偿的方法。通过人工蜂群算法局部搜索最优的能力优化传统反向传播神经网络的权值与阈值,达到减少局部样本陷入极值的目的。将内圈与外圈2组接收光功率以及位移作为训练数据,优化神经网络各参数值,从而建立最优ABC-BP神经网络补偿模型。结果表明人工蜂群算法优化后平均绝对误差减少了0.001 114,均方根误差减少了0.001 182,参数值均小于传统反向传播神经网络和支持向量机补偿模型。对比实验证明该混合算法预测误差更小,能够更高精度完成光强补偿过程。

基于DEB-ABC算法的电动汽车充电优化调度模型

随着电动汽车保有量不断上升,其相关配套设施也面临巨大挑战,不合理的充电资源分配在充电高峰期会造成部分充电站过度拥挤,并且影响电网稳定运行.提出一种考虑多目标优化的调度模型,通过分析充电站内不同充电选项的排队时间,并根据排队率和分时电价提出一种动态定价模型,影响车主充电行为,结合动态定价模型与充电需求计算充电成本,考虑基于起讫点的充电总路径行驶时间,以总成本最少为优化目标,基于DEB-ABC算法进行求解.在某区域内对1500辆电动汽车进行仿真验证,结果表明提出的优化调度模型可减少充电等待时间、充电成本和总行驶时间,提高区域内充电站利用率.

基于GWO-ABC的混合算法研究

大多数种群优化算法面临的共同缺陷是全局搜索能力不足,易陷入局部最优解。文章基于灰狼优化算法和人工蜂群算法,引入混沌映射和OBL策略,提出了新型GWO-ABC混合优化算法。通过GWO-ABC算法优化了FOPID控制器的参数,仿真结果表明,该算法性能优于其它算法。

基于改进人工蜂群算法的船舶管路路径寻优算法分析

[目的]人工蜂群(ABC)算法具有控制参数少、局部寻优能力强、收敛速度快的特点,但在解决路径寻优问题方面,存在容易陷入局部最优的缺陷。为解决船舶管路系统中的管路路径规划问题,提出一种改进的人工蜂群(IABC)算法。[方法]在传统人工蜂群算法的基础上,在跟随蜂的更新机制中引入遗传算子中的交叉操作,并对交叉算子的交叉概率采用自适应的策略;通过对种群进行的交叉操作寻找全局范围内的新解,并改进侦察蜂寻找新路径的方式,由原来的对路径经过的点进行更新改为对路径中的“路段”进行更新;随后,提出一种适应于解决分支管路路径寻优的改进人工蜂群协同进化算法。[结果]实例验证表明,改进后的人工蜂群算法相比标准人工蜂群算法其路径布置效果能够提升32.3%~37.4%,收敛速度能够提升17.7%~29.9%。[结论]无论是解决单管路还是分支管路,改进后的人工蜂群算法相比传统的人工蜂群算法求解质量更高、收敛速度更快、稳定性更好。

基于人工蜂群算法的温差发电阵列最优重构方法

在新能源发电技术快速发展的背景下,温差发电(TEG)技术能够很好地利用新能源发电中产生的废热.然而,温度分布的变化会使得TEG阵列的输出特性恶化、发电效率降低.提出基于人工蜂群(ABC)算法的TEG阵列重构方法,在3种不同温度分布情况下,利用ABC在对称9×9和不对称10×15两种TEG阵列进行动态重构.将所提算法与遗传算法、粒子群优化算法和秃鹰搜索优化算法3种启发式算法作对比,并给出由ABC重构后的TEG阵列温度分布图.结果表明:ABC能够提高TEG阵列的输出功率,输出功率-电压曲线均趋向呈现出单个峰值.此外,利用基于RTLAB平台上的硬件在环实验验证了硬件可行性.

基于流水线计算的3D NoC测试规划研究

为了提高三维片上网络(3D NoC)资源内核的测试效率,提出一种在功耗约束条件下多播流水线并行测试同构核与单播测试异构核相结合的方法对IP核进行测试。为了减少测试数据因资源冲突而进行等待的时间,设计一种改进XYZ路由算法,并采用改进人工蜂群(ABC)算法求解最佳测试规划方案。以国际标准电路测试集ITC'02作为实验对象,结果表明,测试时间最大优化率达到15.45%,与其他测试规划方法相比该文方法能有效地提高并行测试效率。

基于简化人工蜂群算法的地面防空火力拦截设备部署方法

针对地面防空火力拦截设备部署问题中部署方案产生速度慢、不符合战场实际环境问题,提出了一种基于简化人工蜂群算法的地面防空火力拦截设备部署方法。本方法在地面防空火力拦截设备部署方案制定过程中,将人工蜂群算法分为初始化阶段和优化阶段。从专家知识辅助的视角出发,初始化阶段利用专家知识对可部署位置进行了寻优处理并结合随机初始化,优化阶段利用简化型邻域优化对初始化阶段产生的方案进行优化。2个阶段均对产生的部署方案进行校验,若方案达到给定突防概率指标则直接保存输出,大大提高了收敛速度,且产生的部署方案符合实际。仿真结果表明:提出的方法相比于传统蜂群算法在收敛速度方面具有明显优势。

基于ABC-SVM和PSO-RF的光伏微电网日发电功率组合预测方法研究

综合考虑气象因素,使用ABC-SVM方法,对历史的气象数据和光伏出力数据进行训练,依据发电量情况将气象数据分为4类;之后在4类气象情况下各选取上万条数据,使用PSO-RF模型分别训练每组数据,得到4个带不同参数的模型;最后根据每天的气象情况运行不同的模型。验证本组合方法之后发现,通过气象分类后得到的模型,可大幅提高光伏发电量预测的效果。

PSO和ABC的混合优化算法

通过将粒子群优化(Particle Swarm Optimization,PSO)算法与人工蜂群(Artificial Bee Colony,ABC)算法相结合,提出一种ABC-PSO并行混合优化算法。在每次迭代中,将种群分为两个子种群,一个子种群使用PSO算法,另一个子种群使用ABC算法,两个算法寻优后进行比较,选出最优适应值。通过混合算法对4个标准函数进行测试,并与标准PSO算法进行比较,结果表明混合算法具有更好的优化性能。

基于ABC优化算法的神经网络水溶解氧预测

研究水溶解氧预测精确度问题,对指导水厂生产和水产养殖业,为地表水环境的管理提供科学依据。影响水溶解氧量的因素高度关联耦合而难以建立具有普适性的模型,而神经网络由于非线性问题处理能力被广泛应用于溶解氧预测的研究,但是神经网络存在收敛速度慢、网络对初始值敏感、容易陷入局部极小值等缺点而影响预测的精确性和稳定性。为了解决上述问题,在现有算法的基础上,提出了一种人工蜂群算法(ABC)与BP神经网络融合的水溶解氧预测模型。利用ABC算法寻找最优的网络权值和阀值,建立了ABC-BP预测模型对溶解氧进行预测,并分析了输入水质变量对溶解氧的影响权重,最后与遗传优化BP神经网络方法的溶解氧预测结果进行比较。仿真结果表明ABC-BP算法预测精度更高,误差更稳定。

混合排名映射概率和混沌搜索的ABC算法

针对由于人工蜂群算法(Artificial Bee Colony algorithm,ABC)采用直接映射概率选择食物源而引起收敛速度慢、陷入局部最优等问题,提出一种混合排名映射概率和混沌搜索的人工蜂群算法((Artificial Bee Colony algorithm based on Hybrid rank mapping probability and Chaotic search,ABC-HC))。首先,利用目标函数值的排名来获取选择食物源的排名映射概率,并提出计算排名映射概率的两种方法;然后,在观察蜂阶段,融合这两种计算概率的方法,即不同的搜索阶段采用不同的排名映射方法计算食物源选择概率,构造基于混合排名映射概率的人工蜂群算法,以便能够维持种群的多样性避免陷于局部最优;最后,在侦查蜂阶段,使用混沌搜索替代随机搜索以便进一步提高收敛速度,最终获得较好的全局最优解。对10个标准测试函数进行仿真,结果表明,ABC-HC算法不仅提高了收敛速度,而且更能跳出局部最优,有效地找到全局最优解,优于标准的ABC算法和进化算法。

Hadoop平台下粒子滤波结合改进ABC算法的IoT大数据特征选择方法

针对现有物联网大数据特征选择算法计算效率低下、可扩展性不高的问题,提出一种基于改进人工蜂群(ABC)选择特征的系统架构,该架构包含四层体系,可以高效地聚合有效数据,剔除不需要的数据。整个系统是基于Hadoop平台、MapReduce以及改进ABC算法的。改进ABC算法用于选择特征,而MapReduce则由并行算法支持,该算法可高效处理大数据集。该系统使用MapReduce工具实现,并利用粒子滤波来消除噪声。将提出的算法与同类方法进行比较,并通过使用十个不同的数据集对效率、准确性和吞吐量进行评估。结果表明,相比其他几种较新的算法,提出的算法在选择特征时更具可扩展性和高效性。