Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems

A novel population-based algorithm based on the mine bomb explosion concept, called the mine blast algorithm (MBA), is applied to the constrained optimization and engineering design problems. A comprehensive comparative study has been carried out to show the performance of the MBA over other recognized optimizers in terms of computational effort (measured as the number of function evaluations) and function value (accuracy). Sixteen constrained benchmark and engineering design problems have been solved and the obtained results were compared with other well-known optimizers. The obtained results demonstrate that, the proposed MBA requires less number of function evaluations and in most cases gives better results compared to other considered algorithms.     

求解绿色车辆路径问题的离散乌贼算法

为求解绿色车辆路径问题(green vehicle routing problem),提出一种离散乌贼算法(DCOA).采用轮盘赌机制增强初始解选择的随机性,引入精英片段插入策略指导乌贼细胞群的进化方向,提高搜索效率,利用2-opt法和shift法优化当前细胞,增强最优解的局部开发能力.选取Augerat标准数据集,对...     

动态自适应蚁群算法求解TSP问题

针对基本蚁群算法容易出现早熟和停滞现象的缺点,提出一种动态自适应蚁群算法,通过引入信息素的自适应调整策略,限制信息素范围以及动态增加信息素的局部更新方式,有效抑制收敛过程中的停滞现象,提高算法的搜索能力．该算法的性能在中国旅行商问题（China Traveling Salesman Problem,CTSP）和EilSO问题上得到验证．     

求解带时间窗车辆路径问题的混沌遗传算法

针对遗传算法随机性大、末成熟收敛等缺点,提出了将混沌搜索技术和遗传算法相耦合的混沌遗传算法来求解带时间窗的物流配送车辆路径问题(VRPTW)。该算法将混沌变量映射到优化变量的取值范围中,把得到的混沌变量进行编码生成初始种群,然后在遗传操作进行之后对优秀个体增加混沌扰动,促进种群的进化收敛速度,得到最优解。实例计算结果与其他算法比较表明,该算法在求解VRPTW问题时,搜索效率高,能以较快的速度收敛于全局最优解,为求解VRPTW问题提供了一种新方法。     

A distributed adaptive routing algorithm

An algorithm for minimum delay routing in packet-switched networks which is capable of adapting to changes in network input traffic, the addition of new links and nodes, and the failure of existing links and nodes is developed and illustrated. The development is based on theoretical results in Ref.[1] on distributed routing, Newton's method for convex minimization, and the formulation of two separate but cooperating distributed processes which constitute the algorithm itself. The first process, called the normal updating process, provides minimum delay routing given an initial loop-free routing assignment. The second, termed the disturbance adaptive process, generates a new loop-free routing for the first process in response to network changes. The algorithm is applied to a 10 node, 36 link network with 40 commodities, and response characteristics are presented and discussed.     

自适应缓冲控制的移动机器人分布式多跳路由算法

针对移动机器人平台下60GHz无线信号太弱而无法存在于高衰减的长距离或非直线环境的问题,提出了一种基于自适应缓冲控制的分布式鲁棒多跳路由算法。该算法的核心思想是通过求解单位时间平均预期功耗总和最小化和每单位时隙缓冲稳定性的联合随机优化来确定功率分配量,使每个机器人平台能够计算从它自己的缓冲区传输数据包的功率分配量,从而优化整个系统。拥有二十个移动监控机器人平台的仿真实验验证了提出的算法的有效性。实验结果表明,相比分布式多跳路由协议中较为著名的自组织网络按需距离向量(AODV)算法,提出的算法在平均功耗和缓冲占有率方面均有优势,在权重因子为0.5和2时,缓冲占有率分别为AODV的0.14和0.34倍,平均功耗也有所降低。     

一种求解两级车辆路径问题的Memetic 算法

两级车辆路径问题是指物资必须先由中心仓库配送至中转站(第1级),再由中转站配送至客户(第2级)的一种车辆路径问题。针对该NP难问题提出一种Memetic算法通过自底向上的方式进行求解。首先利用改进的最优切割算法MDVRP-Split将客户合理分配至中转站;然后采用局部搜索解决第1级问题,交叉产生的精英个体通过局部搜索改进。标准算例的测试结果表明,所提出算法更注重求解质量与求解效率的平衡,性能优于其他现有的两种算法。     

基于最小跳数的无线传感器网络能量自适应路由算法*

针对基于最小代价的路由算法冗余信息过多和能耗不均衡问题,提出了一种新的路由算法--MHEP算法.新算法通过在网络中建立最小跳数场和路径节点最小能量场,使得信息包可以沿着能耗最优的路径向网关节点发送.通过仿真实验与基于最小代价的路由算法的比较,结果表明该路由算法在能量节省和能耗均衡方面具有明显的优势.     

A competitive neural network algorithm for solving vehicle routing problem

This paper describes a self organization Neural Network algorithm for a class of Vehicle Routing Problems. Motivated by the outstanding performance of adaptive Neural Network approach in the Traveling Salesman Problem, we devised an algorithm to extend the domain of applicability of this approach to more complex problems. First, relevant adaptation is proposed to refine the model for the Multiple Traveling Salesman Problem. Then, an additional mechanism to satisfy further constraints are embodied into the algorithm. The effectiveness of the proposed algorithm is evaluated by considering a series of standard problems from the literature. The results show that the algorithm can yield solutions within a few percent of optimality.     

A bilinear reduction based algorithm for solving capacitated multi-item dynamic pricing problems

In a capacitated multi-item dynamic pricing problem one maximizes the profit by choosing a proper production level as well as pricing policy, where the latter depends on a satisfied demand. The objective function involves inventory, production and setup costs, and revenue functions. The products are required to satisfy joined production capacities. We consider a bilinear reduction of the linear mixed integer formulation of the problem and prove that the problem is equivalent to finding a global maximum of the bilinear problem. A heuristic algorithm is proposed, based on the reduction technique. Numerical experiments confirm the efficiency of the proposed technique.     

A local boosting algorithm for solving classification problems

Based on the boosting-by-resampling version of Adaboost, a local boosting algorithm for dealing with classification tasks is proposed in this paper. Its main idea is that in each iteration, a local error is calculated for every training instance and a function of this local error is utilized to update the probability that the instance is selected to be part of next classifier's training set. When classifying a novel instance, the similarity information between it and each training instance is taken into account. Meanwhile, a parameter is introduced into the process of updating the probabilities assigned to training instances so that the algorithm can be more accurate than Adaboost. The experimental results on synthetic and several benchmark real-world data sets available from the UCI repository show that the proposed method improves the prediction accuracy and the robustness to classification noise of Adaboost. Furthermore, the diversity-accuracy patterns of the ensemble classifiers are investigated by kappa-error diagrams.     

A T-cell algorithm for solving dynamic optimization problems

In this paper, a metaheuristic inspired on the T-Cell model of the immune system (i.e., an artificial immune system) is introduced. The proposed approach (called DTC, for Dynamic T-Cell) is used to solve dynamic optimization problems, and is validated using test problems taken from the specialized literature on dynamic optimization. Results are compared with respect to artificial immune approaches representative of the state-of-the-art in the area. Some statistical analyses are also performed, in order to determine the sensitivity of the proposed approach to its parameters.     

基于自适应蚂蚁算法的动态最优路由选择

蚂蚁算法具有很强的随机性和自适应性，基于蚂蚁圈模型和MMAS模型构造的自适应蚂蚁算法，将网络的容量限制、流量变化和最短距离结合起来讨论，通过在找到的最短路径上设置障碍物来模拟网络拥塞，找到源结点→目的结点的多条最优路由序列，以便在实际中实时地、自适应地进行动态路由选择。     

改进人口迁移算法用于QoS路由优化

研究了带宽、时延等QoS路由问题,针对原人口迁移算法易收敛于局部最优且计算量大,计算时间长的特点,提出了一种改进的人口迁移算法（modified Population Migration Algorithm）,并将其应用于QoS路由选择优化问题。算法采用网络资源消耗和负载分布为目标函数,目的是在消耗网络资源最小的基础上,使负载均衡分布,合理利用网络资源,降低网络拥塞。仿真结果表明,该算法是有效的、稳定的。     

A MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection

Based on the study on communication situation of mobile ad hoc network (MANET) accessing Internet and taking the gateway important function of accessing network into account, a MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection (MRBDAS) is presented. It considers candidate gateways’ connecting degree, load degree, residual energy, and movement rate synthetically and uses the idea of group decision-making method for reference. The algorithm employs the methods of multipaths and query localization technique based on old path information to maintain routing adaptively. Compared with the existing accessing routing algorithm based on dynamic gateway, the algorithm demonstrates in its simulations that by bringing dynamic gateways colony function, the MRBDAS can improve network throughput, reduce average transmission delay of data packets and routing overhead, and prolong accessing network life. The validity of MRBDAS has been proven.     

适用于WSNs的拥塞自适应多径路由算法

针对无线传感器网络(WSNs)数据汇聚特性易导致网络拥塞的问题,结合改进AOMDV协议的多径建立、选择机制的缺陷,提出一种拥塞自适应的多径路由算法。新协议首先引入相关因子模型建立相互干扰度最小的路径集;其次建立路径拥塞信息采集、更新机制,并利用HELLO消息传递。最终源节点通过实时感知路径拥塞信息,自适应选择低拥塞路径来避免拥塞。仿真结果表明:改进的协议显著提高了分组投递速率,降低了端对端时延。     

A MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection

Based on the study on communication situation of mobile ad hoc network (MANET) accessing Internet and taking the gateway important function of accessing network into account, a MANET accessing Internet routing algorithm based on dynamic gateway adaptive selection (MRBDAS) is presented. It considers candidate gateways’ connecting degree, load degree, residual energy, and movement rate synthetically and uses the idea of group decision-making method for reference. The algorithm employs the methods of multipaths and query localization technique based on old path information to maintain routing adaptively. Compared with the existing accessing routing algorithm based on dynamic gateway, the algorithm demonstrates in its simulations that by bringing dynamic gateways colony function, the MRBDAS can improve network throughput, reduce average transmission delay of data packets and routing overhead, and prolong accessing network life. The validity of MRBDAS has been proven.     

An adaptive fault-tolerant routing algorithm for hypercubemulticomputers

This paper presents a partially adaptive fault-tolerant routing algorithm for hypercube multicomputers. The algorithm is tolerant to n-1 link and/or node faults for an n-cube. It makes routing decisions adaptively based on local failure information only. It is simple to implement and needs a very small message overhead. A comparison between the algorithm and a popular previous work is given     

A genetic algorithm for the multiple destination routing problems

The multiple destination routing (MDR) problem can be formulated as finding a minimal cost tree which contains designated source and multiple destination nodes so that certain constraints in a given communication network are satisfied. This is a typical NP-hard problem, and therefore only heuristic algorithms are of practical value. As a first step, a new genetic algorithm is developed to solve the MDR problems without constraints. It is based on the transformation of the underlying network of an MDR problem into its distance complete form, a natural chromosome representation of a minimal spanning tree (an individual), and a completely new computation of the fitness of individual. Compared with the known genetic algorithms and heuristic algorithms for the same problem, the proposed algorithm has several advantages. First, it guarantees convergence to an optimal solution with probability one. Second, not only are the resultant solutions all feasible, the solution quality is also much higher than that obtained by the other methods (indeed, in almost every case in our simulations, the algorithm can find the optimal solution of the problem). Third, the algorithm is of low computational complexity, and this can be decreased dramatically as the number of destination nodes in the problem increases. The simulation studies for the sparse and dense networks all demonstrate that the proposed algorithm is highly robust and very efficient in the sense of yielding high-quality solutions     

An optimal adaptive routing algorithm

An adaptive routing algorithm for packet-switched networks is proposed. This algorithm updates both the estimate of external traffic input and the routing assignments at each iteration. The routing assignments determine the proportions of the traffic destined for nodejto be sent from nodeithrough the outgoing links ofi. The algorithm maintains the loop freedom of the routing assignment at each iteration. It also achieves the minimum delay of the network as the limit of its successive updating procedure. The additional features of the algorithm are that it allows at any iteration some routing assignments which theoretically induce infinite delay and that it may utilize variable scaling factors to speed up the convergence.