焊接条优化设计问题的混沌人工蜂群算法求解

利用混沌的特点设计了一种混沌局部搜索算子,将该算子加入到原始人工蜂群算法中提出了一种混沌蜂群算法(CABC),并用之来优化焊接条的结构设计问题。该问题的目标是在满足约束条件下使得制造焊接条所需的总费用最小,是一个典型多维多约束非线性规划问题。为了不让人工蜂群算法优化该问题时陷入局部最优解,在原始蜂群算法末期的最优值附近进行混沌局部搜索,使其跳出局部最优,有效提高了算法的局部寻优能力。最后对焊接条设计问题进行了仿真计算,并将结果与其他文献中的结果进行了对比,显示了混沌人工蜂群算法优化焊接条设计问题的优越性。     

模糊人工蜂群算法的置换流水车间调度问题求解

针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法.将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新.根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值.通过对置换流水车间调度问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性.     

基于改进粒子群算法求解单级多资源约束生产批量计划问题

本文提出了用于求解单级多资源约束的生产批量计划问题的改进二进制粒子群算法,阐明了算法的具体实现过程。通过对其它文献中的例子进行计算和结果比较,表明了该算法在寻优能力、求解速度和稳定性方面都明显优于文献中的传统遗传算法和退火惩罚混合遗传算法。     

基于混合离散人工蜂群算法的混合零等待柔性流水车间优化研究

从钢铁业等流程工业提炼出一类混合零等待柔性流水车间问题，其中一些加工阶段要求工件连续不断地经过这些工序，对该问题建立了整数规划模型，提出了一种混合离散人工蜂群算法以最小化最大完工时间。采用二维矩阵编码表述染色体以及工件右移调整策略进行解码以获取调度解，改进NEH启发式规则用于生成初始种群。在雇佣蜂阶段，引入了修正粒子群优化算法产生新解；在跟随蜂阶段，设计了迭代贪婪算法中的破坏和构造算子，进一步增强算法的搜索能力；在侦查蜂阶段，利用变邻域搜索算子以替换最差解。对不同规模问题进行了仿真测试并与现有算法进行对比，结果表明所提算法在求解混合零等待柔性流水车间问题方面更加有效。     

求解0-1背包问题的牵制平衡算法

为扩充对于经典NP-hard问题中的0-1背包问题的求解方法，模拟生态系统中各物种间相互依存、牵制，最终达到动态平衡的自然机制，提出一种新型仿生算法：牵制平衡算法。算法以种群规模描述设计变量，以牵制关系为优化驱动力，以系统达到稳态为优化目标，设计了自成长函数、牵制函数、成长函数用以描述设计变量的变化规律，促进解的寻优进程。将牵制平衡算法对于10个不同规模0-1背包问题的求解结果与近年来文献数据进行对比，结果显示算法在8个不同规模的问题中能获得当前已知最优解，验证了牵制平衡算法的收敛性与求解性能，表明算法对于0-1背包问题的求解具有有效性和竞争力。     

任务选择与交换的车辆路线优化问题与BETA求解算法

任务选择与交换的车辆路线优化问题是物流运输中的重要问题.在基于图论的问题转化基础上,建立了问题的混合整数规划精确数学模型.为了对实际大规模的问题进行求解,设计了一种高效的BATA算法.其基本思想是在进行较大规模的局域搜索过程中,算法能够在一个阀值之内接受未作改进的邻域变化解,并在算法进行过程中动态调整接受阀值,从而得到问题的优秀求解结果.通过仿真实验并与已有的算法比较,验证了该算法是可行而有效的.     

基于改进最优觅食算法的锅炉NOx排放预测研究

提出了一种改进的最优觅食算法(POFA),在最优觅食算法中引入自适应惯性权值与全局最优解来改进算法的更新公式,同时加入相空间搜索的机制。利用改进的最优觅食算法优化极端学习机(ELM)构建一个改进的极端学习机模型(POFA-ELM),并用该模型对锅炉NO_x的排放特性进行建模。将该模型与ELM、差分进化算法、粒子群算法、人工蜂群算法以及基本的最优觅食算法优化的ELM模型进行比较。结果表明:该模型的预测精度更好,泛化能力更强,可以更加准确地预测NOx的排放质量浓度。     

求解约束优化问题的退火遗传算法

针对基于罚函数遗传算法求解实际约束优化问题的困难与缺点,提出了求解约束优化问题的退火遗传算法。对种群中的个体定义了不可行度,并设计退火遗传选择操作。算法分三阶段进行,首先用退火算法搜索产生初始种群体,随后利用遗传算法使搜索逐渐收敛于可行的全局最优解或较优解,最后用退火优化算法对解进行局部优化。两个典型的仿真例子计算结果证明该算法能极大地提高计算稳定性和精度。     

基于离散差分进化算法雌多维0／1背包问题求解

多维0／1背包问题（MKP）是一种典型的组合优化问题,并且被广泛的应用于各种工程领域。差分进化算法（DE）是一种有效的进化算法,能处理各种复杂的非线性优化问题,但主要是用来解决连续领域的优化问题。提出一种离散差分进化算法,并用来求解MKP问题。在经典测试集上的实验结果表明,提出的算法能更快的求得最优解。     

基于混合模拟退火算法的多阶段库存路径问题研究

研究单个集散点与多个客户之间的运输问题,综合考虑物流系统的库存成本、运输成本以及卡车的租用成本等因素,以实现总费用的最小化,并将其抽象成多阶段库存路径问题。以每个时间阶段各个客户的订购量为研究对象,采用混合模拟退火算法进行求解,算法中加入了C-W节约算法产生初始解,通过多路径的插入与交换操作来对初始可行解进行改进。数值实验证明了库存路径问题得到的解要优于一般的车辆路径问题得到的解,本文还对库存路径问题中的单位货物库存成本专门进行了分析研究,以确定其取值范围对物流系统总费用的影响。     

Improved artificial bee colony algorithm based on escaped foraging strategy

This paper presents an improved artificial bee colony algorithm. Under the framework of the basic artificial bee colony algorithm, this paper redefines the artificial bee colony and introduces search strategies for group escape and foraging based on Levy flight. The proposed algorithm is named artificial bee colony algorithm based on escaped foraging strategy (EFSABC).There are different strategies for scout bees, onlookers, and free bees searching for honey sources in the EFSABC: all working bees relinquish old honey sources due to disturbance, and select different routines to seek new honey sources. Sixteen typical high-dimensional standard functions are used to verify the effectiveness of the proposed algorithm. The EFSABC algorithm outperforms the traditional artificial bee colony algorithm in all aspects.     

自适应人工蜂群优化极限学习机在拉曼光谱血液定量分析中的应用

提出了一种基于自适应差分进化人工蜂群优化极限学习机预测血液各组分浓度的方法。首先应用人工蜂群算法对输入权值和隐含层阈值迭代寻优;其次结合差分进化进一步提高模型精度且避免后期易陷入局部最优等问题;由于差分进化算法交叉率和变异率存在凭经验给定的不确定性,最后引入了自适应调整的思想提出自适应差分进化人工蜂群算法优化极限学习机算法的模型,将其应用于血液成分定量分析中。实验表明,自适应差分进化人工蜂群算法优化的极限学习机模型具有较高的预测精度,模型具有较强的稳健性。     

基于人工蜂群优化的印刷色彩分色

分析了2种原色油墨叠印后的颜色光谱,并在此基础上建立了印刷中黄、品、青、黑四原色的网点面积率和相互叠印之后的光谱反射率之间的关系模型,结合人工蜂群算法具有良好寻优能力的特点,提出了人工蜂群优化的印刷色彩分色模型。以标准色谱作为测试样本进行了仿真实验,结果表明该模型具有较高的分色精度。     

Improved artificial bee colony algorithm for crack identification in beam using natural frequencies only

An improved artificial bee colony algorithm (I-ABC) is proposed for crack identification in beam structures. ABC is a heuristic algorithm and swarm technique with simple structure, which is easy to implement but with slow convergence rate. In the I-ABC, the differential evolution (DE) mechanism is introduced to employed bee phase, roulette selection strategy is replaced by tournament selection strategy and a new formula is used to simulate onlooker bee’s behaviour. A discrete open crack is used for vibration analysis of the cracked beam and only the changes in the first few natural frequencies are utilized to establish the objective function of the optimization problem for crack identification. A numerical simulation and an experimental work are studied to illustrate the efficiency of the proposed method. Studies show that the present techniques can produce more accurate damage identification results when compared with original ABC, DE algorithm, particle swarm optimization and genetic algorithm.     

基于XFEM和改进人工蜂群算法的结构内部缺陷反演

将扩展有限元法与智能优化算法相结合,基于结构的实际响应值反演出结构内部缺陷信息。传统人工蜂群算法在一定程度上朝着任意的方向搜索,为了避免出现搜索的局部最优现象,该文在传统人工蜂群算法中嵌入了加权平均数突变和交叉算子,将这种改进算法用于单个圆形、椭圆形缺陷和两个不规则缺陷的反演分析,并研究了该算法在测得值有误差情况下的适应性。研究得到:这种改进人工蜂群算法能准确反演出结构的真实缺陷信息;改进人工蜂群算法相比于传统人工蜂群算法收敛速度更快且不易出现局部最优,且定位准确,鲁棒性较强。     

改进人工蜂群算法优化的LSSVM在混合气体定量分析中的应用

针对在易燃易爆混合气体定量分析中因交叉敏感易产生测量误差以及最小二乘支持向量机（least squares support vector machine,LSSVM）参数难以确定的问题,提出一种改进人工蜂群（improved artificial bee colony,IABC）算法优化的最小二乘支持向量机。首先,在标准人工蜂群（artificial bee colony, ABC）算法中引入自适应递减因子以更新步长,并结合轮盘赌和反向轮盘赌改进待工蜂跟随概率公式,从而提高收敛精度;然后,利用改进后的人工蜂群算法对最小二乘支持向量机的惩罚参数C和核参数σ²进行优化;最后,利用优化后的参数重建最小二乘支持向量机定量分析模型,并与利用常用的混合气体定量分析方法——粒子群优化（particle swarm optimization,PSO）算法优化的最小二乘支持向量机定量分析模型进行对比。实验结果表明,在交叉敏感状态下,采用改进人工蜂群算法优化的最小二乘支持向量机时的建模总时间和各组分气体浓度测量的平均相对误差均低于采用粒子群算法优化的,有效提高了混合气体的浓度测量精度。研究表明,改进人工蜂群算法优化的最小二乘支持向量机可为混合气体定量分析提供理论支撑,具有一定的工程应用价值。     

A discrete artificial bee colony algorithm for single machine scheduling problems

This paper presents a discrete artificial bee colony algorithm for a single machine earliness–tardiness scheduling problem. The objective of single machine earliness–tardiness scheduling problems is to find a job sequence that minimises the total sum of earliness–tardiness penalties. Artificial bee colony (ABC) algorithm is a swarm-based meta-heuristic, which mimics the foraging behaviour of honey bee swarms. In this study, several modifications to the original ABC algorithm are proposed for adapting the algorithm to efficiently solve combinatorial optimisation problems like single machine scheduling. In proposed study, instead of using a single search operator to generate neighbour solutions, random selection from an operator pool is employed. Moreover, novel crossover operators are presented and employed with several parent sets with different characteristics to enhance both exploration and exploitation behaviour of the proposed algorithm. The performance of the presented meta-heuristic is evaluated on several benchmark problems in detail and compared with the state-of-the-art algorithms. Computational results indicate that the algorithm can produce better solutions in terms of solution quality, robustness and computational time when compared to other algorithms.     

An efficient hybrid artificial bee colony algorithm for disassembly line balancing problem with sequence-dependent part removal times

This study deals with a sequence-dependent disassembly line balancing problem by considering the interactions among disassembly tasks, and a multi-objective mathematical model is established. Subsequently, a novel hybrid artificial bee colony algorithm is proposed to solve the problem. A new rule is used to initialize a bee colony population with certain diversity, and a dynamic neighbourhood search method is introduced to guide the employed/onlooker bees to promising regions. To rapidly leave the local optima, a global learning strategy is employed to explore higher quality solutions. In addition, a multi-stage evaluation method is designed for onlookers to effectively select employed bees to follow. The performance of the proposed algorithm is tested on a set of benchmark instances and two case scenarios, and the results are compared with several other metaheuristics in terms of solution quality and computation time. The comparisons demonstrate that the proposed algorithm exhibits superior performance.     

A hybrid artificial bee colony algorithm for a flexible job shop scheduling problem with overlapping in operations

Overlapping in operations is an effective technology for productivity improvement in modern manufacturing systems. Thus far, however, there are still rare works on flexible job shop scheduling problems (FJSPs) concerning this strategy. In this paper, we present a hybrid artificial bee colony (hyABC) algorithm to minimise the total flowtime for a FJSP with overlapping in operations. In the proposed hyABC, a dynamic scheme is introduced to fine-tune the search scope adaptively. In view of poor exploitation ability of artificial bee colony algorithm, a modified migrating birds optimisation algorithm (MMBO) is developed and integrated into the search process for better balancing global exploration and local exploitation. In MMBO, a forward share strategy with one-job based crossover is designed to make good use of valuable information from behind solutions. Besides, an improved downward share scheme is adopted to increase diversification of the population, and thus alleviate the premature convergence. Extensive experiments based on benchmark instances with different scales are carried out and comparisons with other recent algorithms identify the effectiveness of the proposed hyABC.     

基于人工蜂群算法与反向传播神经网络的铝热连轧轧制力预测

在分析传统的轧制力数学模型的不足之后,提出了一种基于人工蜂群算法与反向传播神经网络相结合的铝热连轧轧制力预测方法,使用人工蜂群算法优化反向传播神经网络的初始权值和阈值。以现场采集的精轧机组数据作为训练和测试样本,并与Sims数学模型和反向传播神经网络的预测结果进行比较,实验结果表明所提方法的轧制力预测精度和误差明显优于传统算法。