A Robotic-Driven Disassembly Sequence Generator for End-Of-Life Electronic Products

In this study, we propose an intelligent automated disassembly cell for online (real time) selective disassembly. The cell is composed of an industrial robotic manipulator, a camera, range sensing and component segmentation visual algorithms. The cell prototype allows for robotic sensory-driven disassembly under uncertainty. An online genetic algorithm model for selective disassembly is also proposed for optimal and near/optimal disassembly sequencing.     

遗传和禁忌搜索算法在多车物流配送问题中的应用

多车物流配送策略属于物流的统筹配给范畴,应用十分广泛.考虑到遗传算法存在早熟收敛和盲目搜索问题,把禁忌搜索算法独有的记忆思想引入到遗传算法的搜索过程中,将禁忌搜索算法作为遗传算法的变异算子,提出了基于遗传和禁忌搜索的组合算法,并通过与纯遗传算法的比较证实该算法的有效性.     

Multi-Capacity Combinatorial Ordering GA in Application to Cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.     

大规模MIMO系统中优化导频分配方案研究

针对大规模MIMO系统中小区间干扰而导致的导频污染问题,提出了基于IGTSA（Improved Greedy and Tabu Search Algorithm,改进贪婪禁忌搜索算法）和基于CA（Competition Algorithm,竞争算法）的导频分配方案。提出的ITSA是将遗传算法中的变异操作加入禁忌算法来避免其陷入局部最优解,提升全局搜索能力。为更好地实现全局搜索与局部搜索的协调统一,引入CA进一步优化以系统可达和速率达到最大为目标的导频分配方案。相比于局部搜索算法,如GA（Greedy Algorithm,贪婪算法）和TSA（Tabu Search Algorithm,禁忌搜索算法）,所提出的方案能获得更优的系统性能。仿真结果和分析证明了算法的有效性。     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。     

定位—路线问题的遗传禁忌混合优化算法

研究了带软时间窗的定位—路线问题的遗传禁忌混合优化算法,该算法同时兼顾了定位—路线问题中的定位—配给和车辆路线安排两个子问题。给出的遗传算法与禁忌搜索算法的混合策略、遗传编码和相应的遗传操作方式,有效地提高了算法的求解效率和求解质量。最后,通过实验证明了算法的可行性和有效性。     

智能交通系统中的公交运营优化调度研究

城市公交系统是一个巨系统,其相关的模型和方法都非常复杂,为此,本文引入了高性能计算来提高智能算法的优化质量和收敛速度。有机结合遗传算法GA(Genetic Algorithm)和禁忌搜索法TS(Tabu Search)两者优点,构成混合遗传算法HGA(Hybrid Genetic Algorithm)。针对公交车辆调度现状及所处的运营环境,运用HGA的智能化特征,进行了公交车辆智能调度研究。研究表明,基于GA-TS的混合遗传算法优化公交车辆运营调度,能够有效地改善原有公交车辆运营调度的不足,提高动态运营决策效率和服务质量。     

Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.     

An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics

This research presents a technique to obtain production sequences where scheduling flexibility and number of setups are considered. These two objectives of flexibility and setups are typically inversely correlated with each other, so simultaneous optimization of both is challenging. Another challenging issue is the combinatorial nature of such sequencing problems. An efficient frontier approach is exploited where simultaneous maximization of flexibility and minimization of setups is desired. The efficient frontier is constructed via the popular search heuristics of Genetic Algorithms, Simulated Annealing and Tabu Search. For several test problems from the literature, it is discovered that the efficient frontiers obtained via the three search heuristics provide near optimal results for smaller problems, and for larger problems, the Simulated Annealing and Tabu Search approaches outperform the Genetic Algorithm approach.     

基于精英集聚效应的实数编码小世界优化算法

为了提高小世界算法的高维模型优化性能和降低算法的编码复杂性，提出了一种基于精英集聚效应的自适应实数编码小世界优化算法。该算法借鉴小世界现象进行网络空间搜索，包括随机长连接和局部短连接。为了提高优化性能，首先基于精英集聚效应在长连接中加入分级个体吸引策略；然后根据节点优化优劣在短连接中进行搜索次数及邻域大小的自适应调整。为了降低编码复杂性采用了实数编码。最后通过Markov链理论证明了算法的收敛性。数值测试结果表明，与禁忌遗传算法、基本小世界算法以及禁忌小世界算法相比，该算法在相对误差方面平均降低了30.3%，在收敛速度和稳定性方面分别平均提高了18.2%和13.8%，从而验证了算法的有效性。     

解决移动计算位置管理的离散差分进化算法

位置管理问题是移动计算环境中的一个重要问题。提出了一种解决位置管理问题的离散差分进化算法,给出了种群的离散编码方法和一种新的变异操作机制,提出了基于问题特性的种群初始化启发式方法,以及早熟收敛问题的解决策略。基于随机生成的数据对算法进行了模拟实验,将该算法的结果与遗传算法、禁忌搜索算法及蚁群算法进行了对比。     

基于遗传禁忌算法的Ontology划分

为解决企业实际应用中需要部分使用本体（Ontology）的问题,提出一种基于遗传禁忌算法的Ontology自动划分方法。按Ontology划分的要求,将概念被划分进的子Ontology编号组成的数字串作为一条染色体,设计遗传禁忌算法的适应度函数,给出Ontology划分算法的具体步骤。对比实验结果表明,该方法的划分平衡度和准确性优于其他方法。     

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms—a Genetic Algorithm and a Memetic Algorithm with local search—are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.     

求解最小生成树的一种小生境遗传禁忌算法

针对最小生成树问题,提出了一种小生境遗传禁忌算法。算法中使用Prfer数对生成树进行编码。在选择交叉之前使用小生境技术,使得被选中交叉的个体之间的适应值的距离大于一定的阈值,从而保证了个体的多样性。遗传变异算子使用禁忌搜索算法,提高了遗传算法的局部搜索能力,加快了算法的收敛速度。模拟实验结果证明该算法是有效的。     

Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing✰

Remanufacturing helps to reduce manufacturing cost and environmental pollution by reusing end-of-life products. Disassembly is an inevitable process of remanufacturing and it is always finished by manual labor which is high cost and low efficiency while robotic disassembly helps to cover these shortages. Before the execution of disassembly, well-designed disassembly sequence and disassembly line balancing solution help to improve disassembly efficiency. However, most of the research used for disassembly sequence planning and disassembly line balancing problem is only applicable to manual disassembly. Also, disassembly sequence planning and disassembly line balancing problem are separately studied. In this paper, an improved discrete Bees algorithm is developed to solve the collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem. Robotic workstation assignment method is used to generate robotic disassembly line solutions based on feasible disassembly solutions obtained by the space interference matrices. Optimization objectives of the collaborative optimization problem are described, and the analytic network process is used to assign suitable weights to different indicators. With the help of variable neighborhood search, an improved discrete Bees algorithm is developed to find the optimal solution. Finally, based on a gear pump and a camera, case studies are used to verify the effectiveness of the proposed method. The results under different cycle time of robotic disassembly line are analyzed. Under the best cycle time, the performance of the improved discrete Bees algorithm under different populations and iterations are analyzed and compared with the other three optimization algorithms. The results under different assessment methods and scenarios are also analyzed.     

基于遗传算法的最短路径的计算

交通系统中的最优路径算法等同于图论中的最短路径算法,根据不同的具体要求可以是长度最短或行驶时间最短。由于问题的特征、网络特性等的纷繁复杂最短路径算法表现出多样性。除了经典的方法外,近年来出现的模拟退火、Tabu搜索和遗传算法等在优化问题中获得了广泛的应用,本文主要讨论了用改进的遗传算法求解最短路径的方法。     

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.     

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.     

若干优化算法的运行分析比较

研究了若干优化算法在连续空间寻优问题中的应用。通过对标准寻优问题的寻优结果,分析并比较了优化算法的运行效果。考察的性能指标主要有寻优偏差、寻优次数等,分析比较方法采用平均分析法和最优最差分析方法等。文中涉及到的优化算法有遗传算法、模拟退火法、禁忌搜索法、单纯形法和蚁群算法。实验结果表明,单纯形法受解空间的维数以及寻优问题类型的影响较大,而遗传算法、模拟退火法、禁忌搜索法和蚁群算法对各个标准寻优问题则各有所长。对于工程中的实际寻优模型,可将其与标准寻优问题相对照以选择较适合的算法。     

基于遗传和禁忌搜索混合的软硬件划分算法

针对嵌入式系统软硬件划分问题,在比较了遗传算法（GA）和禁忌搜索（TS）各自优缺点的基础上,提出采用遗传/禁忌混合算法（GATS）的策略,用遗传算法提供并行搜索的主框架,用禁忌搜索作为遗传算法的变异算子,遗传算法中变异过程解空间的搜索由禁忌搜索实现。实验结果表明,GATS具有多出发点和记忆功能强、爬山能力强的优势,能够克服GA爬山能力差、TS单点出发的弱点。最后与单纯的遗传算法和禁忌搜索算法进行对比实验,证明GATS更有优势,得到的划分结果也更优秀。