二进制粒子群算法中V型转换函数的应用分析

二进制粒子群算法(BPSO)由于规则简单、参数设置较少等优点被广泛应用到各领域,但是其具有过强的全局搜索能力,缺乏局部的搜索能力等缺陷。针对BPSO存在的缺陷很多文献提出了改进方法,但是针对转换函数的改进较少。通过定义粒子间的距离来分析出BPSO所存在的缺陷,从而进一步分析BPSO中S型转换函数的缺点,并且有针对性地提出更符合BPSO要求的V型转换函数。实验结果表明,所提V型转换函数能克服原始BPSO的缺陷,相比S型转换函数以及现有文献所提的V型转换函数更能提升算法的性能,得到更高的分类准确率。     

A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO

Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TV_T-BPSO. To understand the search behaviour of the TV_T-BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TV_T-BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0–1 knapsack problems, as well as a 200-member truss problem, suggesting that TV_T-BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.     

Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization

Solving the multi-stage portfolio optimization (MSPO) problem is very challenging due to nonlinearity of the problem and its high consumption of computational time. Many heuristic methods have been employed to tackle the problem. In this paper, we propose a novel variant of particle swarm optimization (PSO), called drift particle swarm optimization (DPSO), and apply it to the MSPO problem solving. The classical return-variance function is employed as the objective function, and experiments on the problems with different numbers of stages are conducted by using sample data from various stocks in S&P 100 index. We compare performance and effectiveness of DPSO, particle swarm optimization (PSO), genetic algorithm (GA) and two classical optimization solvers (LOQO and CPLEX), in terms of efficient frontiers, fitness values, convergence rates and computational time consumption. The experiment results show that DPSO is more efficient and effective in MSPO problem solving than other tested optimization tools.     

加速收敛的粒子群优化算法

在基本粒子群优化算法的理论分析的基础上,提出一种加速收敛的粒子群优化算法,并从理论上证明了该算法的快速收敛性,同时对该算法中的参数进行了优化.为了防止其在快速收敛的同时陷入局部最优,采用依赖部分最差粒子信息的变异操作.最后通过与其他几种经典粒子群优化算法的性能比较,表明了该算法的高效和稳健,且明显优于现有的几种经典的粒子群算法.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

A joint antenna and user selection scheme for multiuser MIMO system

In this paper, we present a low-complexity algorithm for real-time joint user scheduling and receive antenna selection (JUSRAS) in multiuser MIMO systems. The computational complexity of exhaustive search for JUSRAS problem grows exponentially with the number of users and receives antennas. We apply binary particle swarm optimization (BPSO) to the joint user scheduling and receive antenna selection problem. In addition to applying the conventional BPSO to JUSRAS, we also present a specific improvement to this population-based heuristic algorithm; namely, we feed cyclically shifted initial population, so that the number of iterations until reaching an acceptable solution is reduced. The proposed BPSO for JUSRAS problem has a low computational complexity, and its effectiveness is verified through simulation results.     

基于改进选择性BPSO算法的配电网降损重构研究

为了降低配电网的有功功率损耗以及提高开关利用率,利用Sigmoid函数对二进制粒子群优化(BPSO)算法中的粒子位置更新进行改进,并通过添加修改曲线的参数对函数进行选择,提出了一种改进选择性二进制粒子群优化(IS-BPSO)算法来求解配电网降损重构问题.该算法以降低配电网的功率损耗为目标,有效地控制粒子的变化速率并提高种群的收敛性.通过33母线和94母线的配电网测试系统进行仿真模拟,结果表明,该算法具有很强的鲁棒性和全局寻优能力.     

一种基于离散微粒群优化的数字曲线的多边形近似算法

数字曲线的多边形近似是图像分析研究领域的一个热点问题.获取数字曲线的优化多边形近似是一个复杂的问题,其计算复杂度非常高.微粒群算法是近些年来提出的一种新的优化方法,已经被广泛应用于各种优化问题的求解.提出了一种求解数字曲线的多边形近似问题的基于整数编码的离散微粒群算法(IPSO).IPSO通过重新定义标准微粒群算法的速度和位置更新公式中的加法、乘法和减法运算,使得算法能运行在离散的解空间.IPSO的位置向量修复机制保证了解的可行性,而局部优化器提高了算法的搜索精度.实验结果表明,IPSO求解的质量和求解的效率均优于遗传算法和0-1编码的微粒群算法.     

Tradeoffs between energy consumption and QoS in mobile grid

Mobile grid, which combines grid and mobile computing, supports mobile users and resources in a seamless and transparent way. However, mobility, QoS support, energy management, and service provisioning pose challenges to mobile grid. The paper presents a tradeoff policy between energy consumption and QoS in the mobile grid environment. Utility function is used to specify each QoS dimension; we formulate the problem of energy and QoS tradeoff by utility optimization. The work is different from the classical energy aware scheduling, which usually takes the consumed energy as the constraints; our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of application satisfaction and consumed energy. It is a more accurate utility model for abstracting the energy characteristics and QoS requirement for mobile users and resources in mobile grid. The paper also proposes a distributed energy–QoS tradeoff algorithm. The performance evaluation of our energy–QoS tradeoff algorithm is evaluated and compared with other energy and deadline constrained scheduling algorithm.     

基于均匀离散PSO 算法的多QoS 网格任务调度策略

针对网格环境中多服务质量(QoS)约束条件下独立任务调度问题,提出一种融合配方均匀设计与离散粒子群优化算法(UDPSO)的任务调度策略,以实现对独立任务优化调度的快速生成.该算法采用类似 DPSO 算法的速度和位置更新方法,结合配方均匀设计,快速衡量各 QoS 约束条件的适应度,以产生分布均匀且较优的 Pareto 解集,最终为系统提供一组较优的任务调度方案.仿真实验表明,该算法更符合网格调度的复杂环境,能够得到较短的任务执行时间和较均衡的 QoS 保障.     

改进粒子群算法应用于Android恶意应用检测

为进行Android恶意应用检测,提取了Android应用程序的API调用信息、申请权限信息、Source-Sink信息为特征,这些信息数量庞大,特征维数高达三四万维。为消除冗余特征和减少分类器构建时间,提出了使用[L1]与离散二进制粒子群算法（BPSO）进行混合式特征选择;同时针对BPSO易早熟收敛的缺点,提出了一种改进的二进制粒子群算法SVBPSO。通过研究不同映射函数对二进制粒子群算法的影响发现,使用S型映射函数的BPSO全局搜索能力强,使用V型映射函数的BPSO局部搜索能力强,故该算法使用S型映射函数进行全局搜索,每隔一定迭代次数使用V型映射函数进行局部探索。实验结果证明,SVBPSO具有良好的收敛效果,使用SVBPSO进行特征选择后能提高Android恶意应用检测正确率。     

基于编码转换的离散演化算法设计与应用

为了利用演化算法求解离散域上的组合优化问题,借鉴遗传算法（GA）、二进制粒子群优化（BPSO）和二进制差分演化（HBDE）中的映射方法,提出了一种基于映射变换思想设计离散演化算法的实用方法——编码转换法（ETM）,并利用一个简单有效的编码转化函数给出了求解组合优化问题的离散演化算法一般算法框架A-DisEA.为了说明ETM的实用性与有效性,首先基于A-DisEA给出了一个离散粒子群优化算法（DisPSO）,然后分别利用BPSO、HBDE和DisPSO等求解集合联盟背包问题和折扣{0-1}背包问题,通过对计算结果的比较表明：BPSO、HBDE和DisPSO的求解性能均优于GA,这不仅说明基于ETM的离散演化算法在求解KP问题方面具有良好的性能,同时也说明利用ETM方法设计离散演化算法是一种简单且有效的实用方法.     

一种基于多条件约束的QoS路由选择优化算法

基于多条件约束的QoS路由选择优化是当前通信网络中的一个重要问题。研究了一类通信网络的源－目的QoS路由选择问题。通过分析,为了不失一般性,选择时延和丢失率为QoS参数,建立了一个带有丢失率约束－条件的最小时延的QoS路由选择的非线性整数规划模型,并根据模型特点,给出了用线性整数规划迭代求精确解的算法。该算法可以方便地推广到多个QoS参数的情况。最后,实例表明所提出的模型和算法是有效的。     

基于改进北极熊算法的制造云服务组合优化

为了提高云制造环境下制造服务组合优化的效率,提出了一种基于改进北极熊算法的制造云服务组合优化方法。该方法对制造服务进行实数编码,并以服务功能和服务质量为评价指标,使用改进的北极熊算法对制造云服务组合优化问题进行求解,得到最优的服务组合方案。同时通过引入动态视野,对算法的局部搜索进行调整,并与遗传算法中的变异策略相结合,以提高求解多目标问题的效率,同时降低因初始参数影响而导致算法陷入局部最优的可能。算例分析表明,改进的北极熊算法在求解制造云服务组合优化问题上比原始北极熊算法、标准遗传算法、改进的灰狼优化算法和改进的粒子群优化算法具有更高的效率。     

基于量子粒子群算法的组播路由优化

不确定网络性能参数下的多约束QoS组播路由优化已成为安全组播领域以及下一代Internet和高性能网络的一个重要研究课题。多约束QoS组播路由优化是NP-完全的多目标优化问题。提出了一个新的量子粒子群算法,其具有收敛速度快、全局性能好等特点。通过应用该算法求解多约束QoS组播路由优化问题的仿真实现,结果表明,该算法取得了较好的效果。     

基于多QoS属性的分类优化调度算法

实现用户的服务质量（Qos）是网格计算中力求达到的重要目标,网格资源的分布性、异构性、动态性等特征使网格环境下以服务质量为指导的资源调度成为一个复杂的问题,尤其是在用户的任务具有多种QoS属性的情况下。该文利用经济模型研究网格QoS控制的资源分配问题。以效用最大化为目标通过综合效用函数量化服务质量,设计了在时间和费用受限情况下对任务进行分类的优化调度算法,该调度算法满足用户多QoS属性。仿真实验显示了该算法的有效性。     

Qos-aware mobile service optimization in multi-access mobile edge computing environments

With the rapid development of mobile Internet technologies and various new service services such as virtual reality (VR) and augmented reality (AR), users’ demand for network quality of service (QoS) is getting higher and higher. To solve the problems of high load and low latency in-network services, this paper proposes a data caching strategy based on a multi-access mobile edge computing environment. Based on the MEC collaborative caching framework, an SDN controller is introduced into the MEC collaborative caching framework, a joint cache optimization mechanism based on data caching and computational migration is constructed, and the user-perceived time-lengthening problem in the data caching strategy is solved by a joint optimization algorithm based on an improved heuristic genetic algorithm and simulated annealing. Meanwhile, this paper proposes a multi-base station collaboration-based service optimization strategy to solve the problem of collaboration of computation and storage resources due to multiple mobile terminals and multiple smart base stations. For the problem that the application service demand in MEC server changes due to time, space, requests and other privacy, an application service optimization algorithm based on the Markov chain of service popularity is constructed, and a deep deterministic strategy (DDP) based on deep reinforcement learning is also used to minimize the average delay of computation tasks in the cluster while ensuring the energy consumption of MEC server, which improves the accuracy of application service cache updates in the system as well as reducing the complexity of service updates. The experimental results show that the proposed data caching algorithm weighs the cache space of user devices, the average transfer latency of acquiring data resources is effectively reduced, and the proposed service optimization algorithm can improve the quality of user experience.     

一种启发式算法在多受限QoS路由中的研究

随着互联网的广泛应用，网络服务质量(QoS)保证技术显得越来越重要，为了保证网络服务质量，希望根据多个QoS约束参数来选择可行路由。一般说来，多受限路径优化问题是一个NP完全问题，因此在多项式时间复杂度里不能解决该问题，针对这个问题，在启发式算法的基础上，提出一种改进扩展Bellman-Ford最短路径算法(MEBF)，将NP完全问题简化为在多项式时间复杂度里能解决的问题。模拟的结果表明，该算法有良好的运行效率和QoS路由成功率。     

Energy‐efficient scheduling algorithms for batch‐of‐tasks (BoT) applications on heterogeneous computing systems

One of the major design constraints of a heterogeneous computing system is optimal scheduling, that is, mapping of tasks on the processing nodes in order to optimize the QoS parameters. Because of the huge energy consumption by computing resources, negative environmental effects and reduced system reliability, energy has unavoidably been added as a new parameter to the list of QoS parameters. Energy optimization in scheduling strategies along with makespan makes it an even more challenging combinatorial optimization problem. This work proposes two energy‐aware scheduling algorithms G1 and G2 to schedule a batch‐of‐tasks, made of a collection of independent tasks, on heterogeneous processors in order to minimize the makespan and the energy consumption. The proposed algorithms schedule tasks based on weighted aggregation cost function to the appropriate processors followed by task migration phase designed to further minimize the makespan and the energy consumption. The study evaluates the performance of the proposed algorithms with some of the peers, that is, MinMin, MINSuff on account of makespan, energy consumption, flowtime, and utilization. An experimental study reveals that the proposed algorithm (G2) consistently performs better under various test conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Prediction of TBM operation parameters using machine learning models based on BPSO

Rational parameters of TBM (Tunnel Boring Machine) are the key to ensuring efficient and safe tunnel construction. Machine learning (ML) has become the main method for predicting operating parameters. Grid Search and optimization algorithms, such as Particle Swarm Optimization (PSO), are often used to find the hyper parameters of ML models but suffer from excessive time and low accuracy. In order to efficiently construct ML models and enhance the accuracy of predicting models, a BPSO (Beetle antennae search Particle Swarm Optimization) algorithm is proposed. Based on the PSO algorithm, the concept of BAS (Beetle Antennae Search) is integrated into the updating process of an individual particle, which improves the random search capability. The convergence of the BPSO algorithm is discussed in terms of inhomogeneous recursive equations and characteristic roots. Then, based on the proposed BPSO prototype, a hybrid ML model BPSO-XGBoost (eXtreme Gradient Boosting) is proposed. We applied the model to the Hangzhou Central Park tunnel project for the prediction of screw conveyer rotational speed. Finally, our model is compared with existing methods. The experimental results show that the BPSO-based model outperforms other traditional ML methods. The BPSO-XGBoost is more accurate than PSO-XGBoost and BPSO-RandomForest for predicting the speed. Also, it is verified that the hyper parameters optimized by the BPSO are better than those optimized by the original PSO. The comprehensive prediction performance ranking of models is as follows: BPSO-XGBoost > PSO-XGBoost > BPSO-RF > PSO-RF. Our models have preferable engineering application value.