An effective hybrid cuckoo search algorithm for constrained global optimization

In recognition of high-quality wideband speech codecs, several standardization activities have been conducted, resulting in the selection of a wideband speech codec called adaptive multi-rate wideband (AMR-WB). The algebraic code-excited linear prediction (ACELP) technique is recommended in AMR-WB, and it is noted that most of the complexity in the ACELP structure comes from the codebook search. In this paper, a new method is proposed for codebook search based on the behavior of backward filtered target signal, d(n), introduced in ITU-T G.722.2 recommendation. To optimize the proposed scheme, five optimization algorithms (i.e., modified genetic algorithm, particle swarm optimization with dynamic inertia weight, bee colony optimization, modified differential evolution, and imperialist competition algorithm) are investigated. Experimental results show that the reduction in codebook search operations of the proposed method is able to reach up to 59 percent as compared with ITU-T G.722.2 recommendation. Meanwhile, BCO-based codebook search scheme has better convergence speed without significant degradation in quality metrics, such as segmental signal-to-noise ratio, mean opinion score, and perceptual evaluation of speech quality, when used in an AMR-WB speech codec.     

An effective memetic differential evolution algorithm based on chaotic local search

This paper proposes an effective memetic differential evolution (DE) algorithm, or DECLS, that utilizes a chaotic local search (CLS) with a ‘shrinking’ strategy. The CLS helps to improve the optimizing performance of the canonical DE by exploring a huge search space in the early run phase to avoid premature convergence, and exploiting a small region in the later run phase to refine the final solutions. Moreover, the parameter settings of the DECLS are controlled in an adaptive manner to further enhance the search ability. To evaluate the effectiveness and efficiency of the proposed DECLS algorithm, we compared it with four state-of-the-art DE variants and the IPOP-CMA-ES algorithm on a set of 20 selected benchmark functions. Results show that the DECLS is significantly better than, or at least comparable to, the other optimizers in terms of convergence performance and solution accuracy. Besides, the DECLS has also shown certain advantages in solving high dimensional problems.     

A solution to the GHI problem for depth-first proof-number search

The Graph-History interaction (GHI) problem is a notorious problem that causes game-playing programs to occasionally return incorrect solutions. This paper presents a practical method to cure the GHI problem for the case of the df-pn algorithm. Results in the game of Go with the situational super-ko rule show that the overhead incurred by our method is very small, while correctness is always guaranteed.     

两层应急抢修系统选址问题的核搜索算法

提出了用于描述两层应急抢修系统选址问题的0-1整数线性规划模型, 该模型能保证整个应急抢修系统的服务质量。设计了求解该问题的两种核搜索算法, 在两种方法中分别根据原问题的线性松弛和拉格朗日松弛确定原问题的核问题和子问题, 从而大大减小了问题的规模。用提出的算法对56个计算实例进行求解, 算例计算结果表明, 与MOSEK软件直接求解得到的结果进行比较, 基于拉格朗日松弛的核搜索算法可以在相对较短的时间内求得较好的解, 这说明拉格朗日松弛对偶问题的最优解能为求解原问题提供非常有效的信息。     

An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems

This paper proposes an effective hybrid tabu search algorithm (HTSA) to solve the flexible job-shop scheduling problem. Three minimization objectives – the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously. In this study, a tabu search (TS) algorithm with an effective neighborhood structure combining two adaptive rules is developed, which constructs improved local search in the machine assignment module. Then, a well-designed left-shift decoding function is defined to transform a solution to an active schedule. In addition, a variable neighborhood search (VNS) algorithm integrating three insert and swap neighborhood structures based on public critical block theory is presented to perform local search in the operation scheduling component. The proposed HTSA is tested on sets of the well-known benchmark instances. The statistical analysis of performance comparisons shows that the proposed HTSA is superior to four existing algorithms including the AL + CGA algorithm by Kacem, Hammadi, and Borne (2002b), the PSO + SA algorithm by Xia and Wu (2005), the PSO + TS algorithm by Zhang, Shao, Li, and Gao (2009), and the Xing’s algorithm by Xing, Chen, and Yang (2009a) in terms of both solution quality and efficiency.     

An effective cuckoo search algorithm for large-scale combined heat and power economic dispatch problem

Neural Computing and Applications - This paper develops an effective cuckoo search algorithm (ECSA) for searching optimal solutions for the problem of combined heat and power economic dispatch. The...     

An effective neighborhood search algorithm for scheduling a flow shop of batch processing machines

This paper considers scheduling problem of flow shop with many batch processing machines and objective of maximum lateness. An effective neighborhood search algorithm (NSA) is proposed for the problem, in which a job permutation and a batch permutation are used to indicate the solution of two sub-problems, respectively. Each job permutation consists of several family-permutations for the representation of jobs from the same family. Two swaps are applied to two permutations to produce new solutions. NSA is applied to a number of instances and compared with some methods, and computational results validate the good performance of NSA.     

An advertisement-based peer-to-peer search algorithm

Most of the existing search algorithms for unstructured peer-to-peer (P2P) systems share one common approach: the requesting node sends out a keyword search query and the query message is repeatedly routed and forwarded to other peers in the overlay network. Due to multiple hops involved in query forwarding, the search may result in a long delay before it is answered. Furthermore, some incapable nodes may be overloaded when the query traffic becomes intensive or bursty.     

一种改进的快速搜索算法

提出了一种基于预测的自适应六边形搜索方法,并将此算法与其他常用的快速运动估计算法进行实验比较。实验结果表明:该算法有效地降低了搜索点数,搜索精度比较接近于FS算法,在一定程度上提高了搜索的效率。     

A two-level self-adaptive variable neighborhood search algorithm for the prize-collecting vehicle routing problem

This paper investigates the prize-collecting vehicle routing problem (PCVRP), which has a strong background in practical industries. In the PCVRP, the capacities of all available vehicles are not sufficient to satisfy the demands of all customers. Consequently it is not a compulsory requirement that all customers should be visited. However, a prize can be collected once a customer is visited. In addition, it is required that the total demands of visited customers should reach a pre-specified value at least. The objective is to establish a schedule of vehicle routes so as to minimize the total transportation cost and at the same time maximize the prize collected by all vehicles. The total transportation cost consists of the total distance of vehicle routes and the sum of vehicles used in the schedule. To solve the PCVRP, a two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm is developed according to the two levels of decisions in the PCVRP, namely the selection of customers to visit and the visiting sequence of selected customers in each vehicle route. The proposed TLSAVNS algorithm is self-adaptive because the neighborhoods and their search sequence are determined automatically by the algorithm itself based on the analysis of its search history. In addition, a graph extension method is adopted to obtain the lower bound for PCVRP by transforming the proposed mixed integer programming model of PCVRP into an equivalent traveling salesman problem (TSP) model, and the obtained lower bound is used to evaluate the proposed TLSAVNS algorithm. Computational results on benchmark problems show that the proposed TLSAVNS algorithm is efficient for PCVRP.     

一种有效的数据分布算法

文章在综合了最佳适应算法、站点全受益法和附加副本算法的基础上,提出了一种有效的数据分布算法,该算法开始依据最小开销确定初始无冗余分布,然后考虑副本站点是否节省费用来确定冗余.     

一种自适应全局和声搜索算法

针对新颖全局和声搜索(NGHS) 算法过早收敛的问题, 提出自适应全局和声搜索(AGHS) 算法. 引入差分向量范数定义和声记忆库多样性, 给出新的位置更新策略, 排除变异操作. 以和声记忆库多样性信息为指导动态产生新和声, 提高算法对解空间信息开发的能力, 避免算法因过早收敛、易陷入局部最优的不足. AGHS算法操作更简单,需要设置的参数更少, 将其与目前文献中较优的几种改进HS 算法、PSO 算法和GA算法进行性能测试, 测试结果表明AGHS算法具有较高的寻优精度和较快的收敛速度.

     

一种实用的标志牌文本提取算法

根据对自然场景图像分割后具有标志牌和背景明显分开等特点,提出了一种基于边框删除的标志牌文本提取算法,首先在二值化图像中采用基于边缘检测和投影算法对标志牌区域进行定位,然后采用边框删除算法提取标志牌文本.大量实验结果表明该方法能够准确定位并提取非规则的标志牌文本.     

An effective suggestion method for keyword search of databases

This paper solves the problem of providing high-quality suggestions for user keyword queries over databases. With the assumption that the returned suggestions are independent, existing query suggestion methods over databases score candidate suggestions individually and return the top-k best of them. However, the top-k suggestions have high redundancy with respect to the topics. To provide informative suggestions, the returned k suggestions are expected to be diverse, i.e., maximizing the relevance to the user query and the diversity with respect to topics that the user might be interested in simultaneously. In this paper, an objective function considering both factors is defined for evaluating a suggestion set. We show that maximizing the objective function is a submodular function maximization problem subject to n matroid constraints, which is an NP-hard problem. An greedy approximate algorithm with an approximation ratio O(\(\frac {1}{1+n}\)) is also proposed. Experimental results show that our suggestion outperforms other methods on providing relevant and diverse suggestions.     

An effective local search for the maximum clique problem

We propose a variable depth search based algorithm, called k-opt local search (KLS), for the maximum clique problem. KLS efficiently explores the k-opt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed in the feasible search space. Computational results on DIMACS benchmark graphs indicate that KLS is capable of finding considerably satisfactory cliques with reasonable running times in comparison with those of state-of-the-art metaheuristics.     

An effective algorithm for delay-constrained dynamic multicasting

With the development of multimedia group applications, the construction of multicast routing tree satisfying Quality of Service (QoS) is more important. In many multicast applications, it is required that the network supports dynamic multicast, which the membership of the multicast group changes with the time. In this paper an effective heuristic algorithm is proposed for dynamic multicast routing with delay-constrained. Aims of this proposed algorithm is to guarantee that: (1) the cost of multicast tree is as small as possible at each node addition/removal event, (2) all the maximal path delay is meet a fixed delay-constrained, (3) minimize perturbation to an existing tree. The proposed algorithm is based on ‘damage’ and ‘usefulness’ concept, and a Balancing Factor (BF) is provided to judge whether or not to arrange a region of tree. Mutation operation in Genetic Algorithm (GA) is also employed to find an attached node in tree for a dynamic adding node. Simulation shows that our algorithm performs well than those static heuristic algorithms in term of cost especially.     

An effective genetic algorithm for network coding

The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm.     

一种应用于故障诊断中的高效推理算法

针对经典联合树推理算法的信息传播共享和推理时间等问题, 提出一种高效联合树推理算法. 该算法基于获得的证据信息和查询节点对原始的网络结构化简, 然后在化简后的网络结构上进行联合树推理. 在信息传递过程中, 该算法可以实现不同证据下的信息共享. 经仿真验证, 高效联合树算法能够在保证准确率的同时, 以更短的时间作出诊断推理. 基于现场收集的数据, 建立水泥回转窑故障诊断系统模型并应用改进的算法实现了精准且快的故障诊断.

     

An effective routing algorithm in incomplete hypercubes

An incomplete hypercube appears interesting and practical because of its relaxed restriction on the system size and possession of salient properties of complete hypercubes. The performance of incomplete hypercubes can be improved considerably by reducing communication time, which can be achieved by forwarding messages through two parallel paths between a pair of nodes. This paper presents a simple and effective two-parallel-paths routing algorithm for incomplete hypercubes which takes advantage of the flexibility provided by incomplete hypercubes, and yet prevents traffic congestion and deadlock. Simulation results indicate that the mean latency for sending large sized messages is reduced and the degree of reduction becomes larger when the system load grows. This significant reduction in latency could translate to a respectable performance improvement. This algorithm can also tolerate one fault in the system by sending duplicate copies of messages through two parallel paths with little increase in the mean latency under light-traffic load.     

一种引入Nelder-Mead算子的改进狼群搜索算法*

为了克服狼群搜索算法(WSA)存在的不足,提出一种新的混合优化算法,称之为引入Nelder-Mead算子的改进狼群搜索算法。该算法使每只狼在搜索中可利用群体信息和个体记忆来指导其搜索猎物,以提高算法的全局搜索能力;让每只狼在搜索中可使用Nelder-Mead方法,以弥补WSA算法在局部搜索能力上的不足。针对12个基准测试实例的实验结果表明, 该算法能够寻得更优的最优解,且鲁棒性更强。