Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem

Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.

Novel Discrete Particle Swarm Optimization Based on Huge Value Penalty for Solving Engineering Problem

For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization（DPSO） is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization（PSO）, but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization（DPPSO） and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.

A New Clustering Algorithm Using Adaptive Discrete Particle Swarm Optimization in Wireless Sensor Network

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.

Service composition based on discrete particle swarm optimization in military organization cloud cooperation

This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.

RESEARCH ON OPTIMIZING THE MERGING RESULTS OF MULTIPLE INDEPENDENT RETRIEVAL SYSTEMS BY A DISCRETE PARTICLE SWARM OPTIMIZATION

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

Optimizing the Multi-Objective Discrete Particle Swarm Optimization Algorithm by Deep Deterministic Policy Gradient Algorithm

Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.

Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles （UCAVs） is a complicated global optimum problem. Particle Swarm Optimization （PSO） is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Diseretization （CPTD） method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.

Dynamic Weapon Target Assignment Based on Intuitionistic Fuzzy Entropy of Discrete Particle Swarm

Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.

Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning

The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization was developed,which is suitable for power transmission network expansion planning, and requires less computer s memory.The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzod.Numerical simulation demonstrated the effectiveness and correctness or the method. This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

矩形件排样优化问题的DPSO-CG混合优化算法研究

针对矩形件排样问题非线性、高复杂计算性等特点,提出一种结合离散粒子群优化(DPSO)算法和混沌遗传(CG)策略的DPSO-CG混合优化算法。利用混沌运动的遍历性和随机性,引入混沌交叉和混沌变异的遗传操作,通过增加个体的多样性,增强算法全局搜索能力,结合最低水平线定位算法,实现矩形件排样后板材利用率的提高。最后针对实例进行排样优化验证,排样结果表明,DPSO-CG混合优化算法能够使板材利用率最大值达到0.9417,实现更优的排样,验证了算法的正确性和有效性。

电力系统机组启停优化问题的改进DPSO算法

该文从微粒群优化算法的原理和机组组合问题的特点出发,提出了一种适合机组启停优化问题求解的改进的离散二进制微粒群优化算法(DPSO):文中结合机组启停优化问题的特点,采用改进的DPSO 算法对机组的开停机状态进行优化组合,利用随机的顺序投入法初始化原始种群,将无希望/重希望准则引入搜索过程,通过重新初始化机制与变异操作克服DPSO 易于陷入局部最优的缺点,并保证机组的开停状态组合满足单机约束和系统约束。保证搜索在问题的可行域进行。对2 个算例系统的仿真计算及与其它方法的比较表明,该算法在搜索精度和搜索速度方面均具有很大的优越性。此算法兼顾了收敛速度和收敛精度2 个方面,具有很好的适应性。这种寻优的方式不仅为机组启停优化问题带来了新的解决思路,对于求解更广泛的组合优化问题亦具有普遍的意义。

基于贪婪度表的DPSO求解舰船电力系统网络重构

针对舰船电力系统网络重构问题,提出一种基于贪婪度表的离散微粒群算法。该方法采用概率贪婪法对种群离散化,在迭代之前先给出贪婪度表,迭代中计算概率时直接取贪婪度表中的贪婪度值,避免计算量过度增加。调整了贪婪度函数计算公式。限制了贪婪度及概率的大小,避免算法早熟收敛。对算法离散过程进行了分析。舰船电力系统网络重构算例显示,该方法具有优良的搜索性能。

基于DPSO算法以负荷恢复为目标的网络重构

研究了大停电事故后输电系统的重构优化问题,提出了一种求解最优目标网的离散粒子群优化(DPSO)算法。将网络重构问题表示为以重要负荷恢复量占已恢复负荷总量的比例最高为目标的非线性优化问题,在求解目标网时考虑了负荷重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。该算法在求解输电网重构问题时,编码容易且能方便地处理网络连通性问题,求解效率高、速度快。在IEEE57节点系统和IEEE118节点系统中的应用结果验证了文中方法的有效性。

求解舰船电力系统网络重构的贪婪DPSO算法

针对舰船电力系统的网络重构,建立了故障恢复的离散模型.提出基于简单贪婪法、概率贪婪法两种离散微粒群优化算法,分析了参数对离散化过程的影响.舰船电力系统网络故障恢复算例显示:该方法能获得更好的故障恢复方案;参数选取合适的概率贪婪法能有效地克服微粒群算法易于陷入局部极值的缺点,具有优良的收敛性能.

云计算环境下的DPSO资源负载均衡算法

负载均衡问题是云计算研究的热点问题之一。运用离散粒子群算法对云计算环境下的负载均衡问题进行研究,根据云计算环境下资源需求动态变化,并且对资源节点服务器的要求较低的特点,把各个资源节点当做网络拓扑结构中的各个节点,建立相应的资源-任务分配模型,运用离散粒子群算法实现资源负载均衡。验证表明,该算法提高了资源利用率和云计算资源的负载均衡。

基于DPSO的改进AO^＊算法在大型复杂电子系统最优序贯测试中的应用

针对大型复杂电子系统最优序贯测试问题,提出一种基于离散粒子群算法（DPSO）和改进AO^＊算法相结合的方法.DPSO优化AO^＊算法中每个要扩展节点的测试集从而减少测试个数;改进AO^＊算法通过规定扩展节点估价值的范围,减少其回溯次数.实例验证表明,该算法不仅有效地降低了计算复杂度,大大减少测试代价,缩短测试时间,而且避免了原有AO^＊算法当备选的测试集太大时容易出现“计算爆炸”的缺点.

基于DPSO负载可控的虚拟网络映射算法

针对多租赁模式下的虚拟网络映射问题,以降低底层链路负载、加快映射速度、提高底层物理资源利用率为目标,将离散粒子群算法与虚拟节点映射规则相结合,提出了物理节点可复用、负载可控制的MLB-VNE-SDPSO算法.该算法在兼顾CPU等主机资源利用率的前提下节约了物理链路的带宽资源,缩短了虚拟链路的映射过程.仿真实验表明,在保证网络负载的前提下,获得了较好的物理节点利用率,提高了虚拟网络的收益成本比.

基于MODPSO算法的FPRM电路多约束极性优化方法

为求解较大规模FPRM逻辑电路中多约束条件下的极性优化问题,该文提出一种基于多目标离散粒子群优化(Multi-Objective Discrete Particle Swarm Optimization,MODPSO)算法的求解方法。首先针对FPRM电路极性设计需要满足延时短、面积小的多约束要求,构建了多目标决策模型。然后结合极性转换算法和MODPSO算法,对电路进行最优极性搜索,以获取电路延时和面积的Pareto最优解集。最后利用17个MCNC Benchmark电路进行测试,并将MODPSO算法与DPSO算法、NSGA-Ⅱ算法进行实验对比,结果验证了算法的有效性。

云环境下基于DPSO的任务调度算法

针对云计算任务调度问题,结合粒子群优化(PSO)算法的种群个体协作和信息共享特点,提出一种基于离散粒子群优化(DPSO)的任务调度算法。采用随机方法生成初始种群,利用时变方式调整惯性权重,并在位置更新中使用绝对值取整求余映射法进行合法化处理,提高PSO算法的离散化程度。搭建并重新编译了CloudSim云计算仿真平台进行实验,结果显示,当迭代次数为200时,DPSO、PSO、GA算法的所有任务最终调度时间分别为457.69 s、467.90 s、472.41 s,从而证明DPSO算法能够有效解决云计算环境下的任务调度问题,并且算法收敛速度优于PSO和GA算法。