A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations

This paper presents a novel hybrid optimization approach based on differential evolution algorithm and receptor editing property of immune system. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing industry. The proposed hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of hybrid particle swarm algorithm, ant colony algorithm, immune algorithm, hybrid immune algorithm, genetic algorithm, feasible direction method and handbook recommendation.     

An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers

In this paper, an effective hybrid discrete differential evolution (HDDE) algorithm is proposed to minimize the maximum completion time (makespan) for a flow shop scheduling problem with intermediate buffers located between two consecutive machines. Different from traditional differential evolution algorithms, the proposed HDDE algorithm adopted job permutation to represent individuals and applies job-permutation-based mutation and crossover operations to generate new candidate solutions. Moreover, a one-to-one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation. In addition, an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the HDDE algorithm to enhance the algorithm’s local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It shows that the proposed HDDE algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm, but outperforms two recently proposed discrete differential evolution (DDE) algorithms as well. Especially, the HDDE algorithm is able to achieve excellent results for large-scale problems with up to 500 jobs and 20 machines.     

A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem.A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.     

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

一种具有混合编码的二进制差分演化算法

差分演化(DE)是Storn和Price于1997年提出的一种基于个体差异重组思想的演化算法,非常适用于求解连续域上的最优化问题.首先引入"差异算子"等概念,给出DE的一种简洁算法描述,并分析了它所具有的特性.然后,为了使DE能够求解离散域上的最优化问题,基于数学变换思想引入"辅助搜索空间"和"个体混合编码"等概念,通过定义一个特殊的满射变换,在辅助搜索空间的作用下将连续域上的高效差分演化搜索变换为离散域上的同步演化搜索,由此提出了第1个二进制差分演化算法:具有混合编码的二进制差分演化算法(HBDE).接着,给出了HBDE的依概率收敛和完全收敛的定义,并利用离散Markov随机理论证明了HBDE是完全收敛的. HBDE不仅完全具有DE的各种特性和所有优点,而且非常适用于求解离散域上的最优化问题,对随机生成的大规模3-SAT问题实例和典型0/1背包问题实例的数值计算表明:该算法具有很好的全局收敛性和稳定性,其性能远远超过二进制粒子群优化算法和遗传算法.     

协同智能的蝙蝠差分混合算法

为解决差分进化算法后期收敛易陷入局部最优和早熟收敛的问题,提出一种群体智能优化算法,即协同智能的蝙蝠差分混合算法。利用蝙蝠个体脉冲回声定位的特点,与差分种群相互协作,在当前最优解gbest附近进行一次详细搜索,有效增加种群的多样性,跳出局部最优。通过蝙蝠种群和差分种群两个种群的相互协作,较好平衡全局搜索和局部开发之间的能力。为验证算法有效性,选用9个常用的基准测试函数和5个0-1背包问题,与标准粒子群算法、带高斯扰动的粒子群算法、蝙蝠算法、差分算法、烟花算法相对比,仿真实验表明,所提算法总体性能优于其它5种算法。     

基于粒子群算法和序贯搜索的高光谱波段选择

波段选择是降低高光谱数据量,克服地物分类中Hughes现象的有效手段。子集生成方式和评价准则是选择算法的两要素。提出一种混合随机搜索与启发式搜索的子集生成方法。该方法在随机搜索中嵌入启发式搜索,对由离散粒子群优化算法每次迭代更新的种群利用序贯搜索进行局部微调,提高了随机搜索的精度。这种嵌入微调也保证了优化算法解的有效性。高光谱波段选择与分类实验比较了该方法与混合遗传算法、标准遗传算法和顺序前向浮动选择算法的性能,表明算法能选择出评价准则意义下更好的子集。     

数据驱动选择策略的多目标差分进化算法

针对多目标差分进化算法求解复杂多目标优化问题时,最优解选择策略中非支配排序计算复杂度高的问题,提出一种数据驱动选择策略的多目标差分进化(MODE-DDSS)算法.首先,设计多目标差分进化算法的优化解排序等级评估准则,建立基于评估准则的优化解排序等级评估库;其次,设计基于优化解双向搜索机制和无重复比较机制的数据驱动选择策略,实现优化解的高效搜索和快速排序;最后,构建数据驱动选择策略的多目标差分进化算法,降低算法在最优解选择操作中的时间复杂度,提高算法的寻优效率.实验结果表明,所提出的MODE-DDSS算法能够有效减少最优解在选择过程中的比较次数,提升多目标差分进化算法解决复杂多目标优化问题的寻优效率.     

基于滤子混合协同进化算法的无功优化

混合整数非线性规划问题存在于大量工程和管理中,针对此问题提出一种滤子混合协同进化算法.利用滤子技术代替罚函数处理约束条件,采用混合编码和由差分进化算法与遗传算法异构的种群协同解决混合整数变量问题,引入基于平均熵和Logistic混沌初始化增加算法鲁棒性,利用自适应缩放因子和精英交流学习策略构成策略协同,与种群协同耦合,以提高算法搜索能力.以IEEE30节点测试系统进行无功优化为例,仿真结果表明所提出的算法具有全局搜索能力和有效性.     

Comparison of evolutionary-based optimization algorithms for structural design optimization

In this paper, a comparison of evolutionary-based optimization techniques for structural design optimization problems is presented. Furthermore, a hybrid optimization technique based on differential evolution algorithm is introduced for structural design optimization problems. In order to evaluate the proposed optimization approach a welded beam design problem taken from the literature is solved. The proposed approach is applied to a welded beam design problem and the optimal design of a vehicle component to illustrate how the present approach can be applied for solving structural design optimization problems. A comparative study of six population-based optimization algorithms for optimal design of the structures is presented. The volume reduction of the vehicle component is 28.4% using the proposed hybrid approach. The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.     

混合遗传算法及在多处理机调度问题中的应用

针对遗传算法存在收敛速度较慢,易陷入局部极值的缺点,通过算法混合,提出一种基于混沌及差分演化的混合遗传算法。该算法利用混沌运动的遍历性和内在随机性择优产生初始群体,借鉴差分进化算法中的繁殖策略,使染色体在解空间中更有效的搜索最优解。最后将该混合遗传算法应用于多处理机调度问题中,实验表明,该混合算法具有较高的优化效率,能寻找到更好的优化结果。     

基于差分和状态空间遗传混合算法的信号配时优化

为了最大限度地挖掘现有道路的承载能力,提出了一种基于差分进化算法和状态空间模型遗传算法的两阶段混合优化算法,建立以车辆平均等待时间最小为目标的数学模型进行优化。为了解决差分进化算法在后期收敛速度变慢,容易陷入局部最优的缺点,引入改进后的状态空间模型遗传算法形成一种混合算法。然后,用所提出的混合算法对5个经典测试函数进行寻优测试,并与定时控制、差分进化算法以及状态空间模型遗传算法进行对比,实验结果表明该混合算法不仅提高了收敛速度,并且在保证了算法收敛精度的前提下缩短了迭代次数。最后,以单交叉路口为例,验证该混合算法在求解信号灯配时问题时的优化效果。     

Bearing parameter identification of rotor–bearing system using clustering-based hybrid evolutionary algorithm

A new bearing parameter identification methodology based on global optimization scheme using measured unbalance response of rotor–bearing system is proposed. A new hybrid evolutionary algorithm which is a clustering-based hybrid evolutionary algorithm (CHEA), is proposed for global optimization scheme to improve the convergence speed and global search ability. Clustering of individuals by using a neural network is introduced to evaluate the degree of mature of genetic evolution. After clustering-based genetic algorithm (GA), local search is carried out for each cluster to judge the convexity of each cluster. Finally, random search is adapted for extrasearching to find a potential global candidate, which could be missed in GA and local search. The proposed methodology can identify not only unknown bearing parameters but also unbalance information of disk by simply setting them as unknown parameters. Numerical example and experimental results were used to verify the effectiveness of the proposed methodology.     

一种基于差分进化和免疫克隆选择算法的混合优化方法

为了提高免疫克隆选择算法的搜索能力,提出了一种基于差分进化和免疫克隆选择算法的混合优化方法。该方法采用差分进化提高免疫克隆选择算法的抗体亲和度,并对该算法的收敛性进行了分析。为了测试该算法的有效性,将该算法应用于函数优化问题中。仿真结果表明,该方法具有更高的收敛速度和收敛精度。     

Two hybrid differential evolution algorithms for engineering design optimization

This paper presents two hybrid differential evolution algorithms for optimizing engineering design problems. One hybrid algorithm enhances a basic differential evolution algorithm with a local search operator, i.e., random walk with direction exploitation, to strengthen the exploitation ability, while the other adding a second metaheuristic, i.e., harmony search, to cooperate with the differential evolution algorithm so as to produce the desirable synergetic effect. For comparison, the differential evolution algorithm that the two hybrids are based on is also implemented. All algorithms incorporate a generalized method to handle discrete variables and Deb's parameterless penalty method for handling constraints. Fourteen engineering design problems selected from different engineering fields are used for testing. The test results show that: (i) both hybrid algorithms overall outperform the differential evolution algorithms; (ii) among the two hybrid algorithms, the cooperative hybrid overall outperforms the other hybrid with local search; and (iii) the performance of proposed hybrid algorithms can be further improved with some effort of tuning the relevant parameters.     

Hybrid krill herd algorithm with differential evolution for global numerical optimization

In order to overcome the poor exploitation of the krill herd (KH) algorithm, a hybrid differential evolution KH (DEKH) method has been developed for function optimization. The improvement involves adding a new hybrid differential evolution (HDE) operator into the krill, updating process for the purpose of dealing with optimization problems more efficiently. The introduced HDE operator inspires the intensification and lets the krill perform local search within the defined region. DEKH is validated by 26 functions. From the results, the proposed methods are able to find more accurate solution than the KH and other methods. In addition, the robustness of the DEKH algorithm and the influence of the initial population size on convergence and performance are investigated by a series of experiments.     

A hybrid approach of differential evolution and artificial bee colony for feature selection

“Dimensionality” is one of the major problems which affect the quality of learning process in most of the machine learning and data mining tasks. Having high dimensional datasets for training a classification model may lead to have “overfitting” of the learned model to the training data. Overfitting reduces generalization of the model, therefore causes poor classification accuracy for the new test instances. Another disadvantage of dimensionality of dataset is to have high CPU time requirement for learning and testing the model. Applying feature selection to the dataset before the learning process is essential to improve the performance of the classification task. In this study, a new hybrid method which combines artificial bee colony optimization technique with differential evolution algorithm is proposed for feature selection of classification tasks. The developed hybrid method is evaluated by using fifteen datasets from the UCI Repository which are commonly used in classification problems. To make a complete evaluation, the proposed hybrid feature selection method is compared with the artificial bee colony optimization, and differential evolution based feature selection methods, as well as with the three most popular feature selection techniques that are information gain, chi-square, and correlation feature selection. In addition to these, the performance of the proposed method is also compared with the studies in the literature which uses the same datasets. The experimental results of this study show that our developed hybrid method is able to select good features for classification tasks to improve run-time performance and accuracy of the classifier. The proposed hybrid method may also be applied to other search and optimization problems as its performance for feature selection is better than pure artificial bee colony optimization, and differential evolution.     

融合智能算法的变形体碰撞检测算法研究

针对变形体碰撞检测算法的准确性与实时性问题,提出了一种融合智能算法的变形体碰撞检测算法。在随机碰撞检测的基础上,使用层次包围技术缩小粒子搜索空间,采用一种融合基于量子行为的粒子群算法与差分进化算法的混合智能算法进行搜索。该方法以局部吸引子作为差分变异基础,在扩大种群多样性的同时加快了算法收敛速度,有效地解决了传统智能算法不适应离散空间计算问题以及早熟收敛问题。针对随机碰撞粒子搜索空间特点,混合算法的引入大大提高了碰撞检测算法的检测效率,解决了检测过程中的穿刺与遗漏现象。经实验验证该方法在很大程度上提高了变形体碰撞检测的实时性与准确性。     

An efficient hybrid learning algorithm for neural network–based speech recognition systems on FPGA chip

This paper implemented an artificial neural network (ANN) on a field programmable gate array (FPGA) chip for Mandarin speech measurement and recognition of nonspecific speaker. A three-layer hybrid learning algorithm (HLA), which combines genetic algorithm (GA) and steepest descent method, was proposed to fulfill a faster global search of optimal weights in ANN. Some other popular evolutionary algorithms, such as differential evolution, particle swarm optimization and improve GA, were compared to the proposed HLA. It can be seen that the proposed HLA algorithm outperforms the other algorithms. Finally, the designed system was implemented on an FPGA chip with an SOC architecture to measure and recognize the speech signals.