基于量子粒子群优化算法的车辆路径问题

针对粒子群优化算法的搜索空间有限、容易出现早熟现象的缺陷,提出将一种基于量子行为的粒子群优化算法用于求解车辆路径问题.车辆路径问题是组合优化问题中的NP-难问题.将量子粒子群算法用于车辆路径问题求解,用粒子的位置表示车辆路径,建立车辆路径的数学模型.与粒子群算法相比,量子粒子群算法提高了最优路径搜索的成功率,能更有效的求解问题.     

改进遗传操作PSO算法及其在VRP中的应用

针对车辆路径问题(Vehicle Routing Problem,VRP)是物流系统调度中的关键环节,对车辆路径进行优化调度,可以提高物流经济效益,实现物流科学化.为优化路径,提高搜索效率,针对车辆路径优化问题的特殊性,提出一种改进信息循环利用机制的遗传操作微粒群算法,利用准连续编码算法对解窄间进行编码,引入一种信息保留机制,把所有粒子自身的信息保留为一个信息库,利用信息库对粒子进行重组,实现粒子的更新,从而提高算法搜索的效率.通过仿真表明,在VRP的求解上与遗传算法和双种群遗传算法进行对比研究,证明了微粒群算法是一个求解VRP的较好方案.     

基于混沌和差分进化的混合粒子群优化算法

研究粒子群算法优化问题,由于标准粒子群优化算法(PSO)在高维复杂函数优化中易早收敛,影响全系统优化。为改进的混合粒子群优化算法,提出了一种基于混沌和差分进化的混合粒子群优化算法(CDEHPSO)。把基于Logistic映射的混沌序列引入到种群初始化操作中。在算法进化过程中,通过一种粒子早熟判断机制,在基本粒子群优化算法中引入了差分变异、交叉和选择操作,对早熟粒子个体进行差分进化操作,从而维持了种群的多样性并有效避免了算法陷入局部最优。仿真结果表明,相比于粒子群优化算法和差分进化算法(DE),CDEHPSO算法具有收敛速度快、搜索能力强的优点。     

粒子群优化算法的收敛性分析及其混沌改进算法

分析了粒子群优化算法的收敛性,指出它在满足收敛性的前提下种群多样性趋于减小,粒子将会因速度降低而失去继续搜索可行解的能力;提出混沌粒子群优化算法,该算法在满足收敛性的条件下利用混沌特性提高种群的多样性和粒子搜索的遍历性,将混沌状态引入到优化变量使粒子获得持续搜索的能力.实验结果表明混沌粒子群优化算法是有效的,与粒子群优化算法、遗传算法、模拟退火相比,特别是针对高维、多模态函数优化问题取得了明显改善.     

基于改进量子PSO算法的可约束车辆路径优化

为了求解有能力约束的车辆路径优化问题,提出了用于可约束车辆路径优化的改进量子PSO算法。首先给出了车辆路径的数学模型,介绍了粒子群算法;然后提出了改进量子粒子群算法,该算法采用了2-opt.1-1交换等局部优化算法那进行线路内和线路间的优化,引入种群熵算法的方法来衡量算法那是否陷入局部最优,采用灾变的操作保证解得多样性,并给出了该算法用于求解车辆路径的有关问题的具体方法;通过与别的算法的比较并结合仿真实验,有效地说明了该算法的可行性和有效性。     

改进型PSO算法在VRP中的应用

运用罚函数法将约束优化问题转化为无约束优化问题,同时采用实数编码方案,将离散的车辆路径问题转化成准连续优化问题,在此基础上,用改进的粒子群优化算法求解最优值.改进的粒子群算法引入了杂交PSO模型和变异算子.仿真实验结果表明,该算法在保持粒子种群多样性、提高收敛速度和搜索精度、扩大搜索范围、避免过早收敛于局部极值点等方面...     

改进的混沌粒子群算法求解车辆路径问题*

为求解车辆路径问题提出一种改进的混沌粒子群优化算法。该算法在基本混沌粒子群优化算法（CPSO）基础上,引入逻辑斯特函数,对惯性权重因子w进行非线性调整,提高了算法的寻优能力,有效避免了算法陷入局部最优并防止过早收敛。采用该算法应用于车辆路径问题,仿真结果表明该与标准遗传和双种群遗传算法比较,具有一定的优势。     

优化复杂函数的粒子群-鸽群混合优化算法

针对复杂函数优化问题,提出一种两阶段混合优化算法。对基本粒子群和鸽群算法进行改进,引入惯性因子和跳跃算子增强了粒子群算法的搜索能力,提出干扰算子增加了鸽群算法的种群多样性。将改进后的两种算法相结合,形成两阶段混合优化算法,同时定义了一种多样性函数对种群进行实时监测,以保证种群的多样性。采用两组经典测试函数,对算法性能进行测试。结果表明,算法适用于求解复杂函数优化问题,且具有较好的收敛速度和收敛精度。     

基于EM算法和PSO算法的混合优化算法

针对粒子群优化算法容易陷入局部最优解并且存在过早收敛的问题,将类电磁机制算法中的吸引-排斥机制引入到粒子群优化算法中,提出一种类电磁机制算法和粒子群优化算法的混合优化算法(EMPSO).首先按照基本粒子群优化算法的寻优方式对各粒子进行更新,再利用类电磁机制中的吸引-排斥机制对个体最优粒子和群体最优粒子进行移动,最后通过几个标准测试函数进行了测试,并与标准粒子群算法(PSO)、免疫粒子群算法(IPSO)、混沌粒子群算法(CPSO)进行对比.测试结果表明,改进算法提高了全局搜索能力和熟练速度,改善了优化性能.     

改进的变尺度混沌粒子群算法及其应用

针对粒子群算法(PSO)存在局部最优及后期收敛速度慢等问题,提出一种改进的变尺度混沌粒子群算法(IMCPSO).该算法初期,在整个解空间对最优粒子进行变尺度混沌扰动,以防止陷入局部最优;算法后期,则以最优粒子为中心引入变尺度混沌扰动,以提高算法收敛速度.当算法一旦陷入局部最优时,采用混沌粒子替代部分种群粒子以增加粒子多样性,使算法尽快跳出局部最优.基于benchmark测试函数的仿真结果表明,所提算法与基本粒子群算法(SPSO)和变尺度混沌粒子群算法(MCPSO)相比,具有明显好的搜索精度和收敛速度.最后,将该算法应用于电路故障诊断实验中的支持向量机参数优化问题,实验结果说明了其应用价值.     

基于离散微粒群算法求解背包问题研究

微粒群算法(PSO)是一种新的演化算法,主要用于求解数值优化问题.基于离散微粒群算法(DPSO)分别与处理约束问题的罚函数法和贪心变换方法相结合,提出了求解背包问题的两个算法:基于罚函数策略的离散微粒群算法(PFDPSO)和基于贪心变换策略的离散微粒群算法(GDPSO).通过将这两个算法与文献[7]中的混合微粒群算法(Hybrid_PSO)进行数值计算比较发现:对于求解大规模的背包问题,GDPSO非常优秀,其求解能力优于Hybrid_PSO和PFDPSO,是求解背包问题的一种非常有效的方法.     

改进粒子群算法在单体型重构问题中的应用

结合单体型装配问题的计算模型—最少错误纠正模型（MEC）的特定知识,提出了一种求解单体型装配问题的改进粒子群算法。应用改进粒子群算法对真实数据和模拟数据进行数值计算,并且与基础粒子群算法和遗传算法进行比较,数值结果表明所设计的改进粒子群算法在单体型重构率上优于基础粒子群算法和遗传算法。     

A new approach for solving set covering problem using jumping particle swarm optimization method

The set covering problem (SCP) is a well known classic combinatorial NP-hard problem, having practical application in many fields. To optimize the objective function of the SCP, many heuristic, meta heuristic, greedy and approximation approaches have been proposed in the recent years. In the development of swarm intelligence, the particle swarm optimization is a nature inspired optimization technique for continuous problems and for discrete problems we have the well known discrete particle swarm optimization (DPSO) method. Aiming towards the best solution for discrete problems, we have the recent method called jumping particle swarm optimization (JPSO). In this DPSO the improved solution is based on the particles attraction caused by attractor. In this paper, a new approach based on JPSO is proposed to solve the SCP. The proposed approach works in three phases: for selecting attractor, refining the feasible solution given by the attractor in order to reach the optimality and for removing redundancy in the solution. The proposed approach has been tested on the benchmark instances of SCP and compared with best known methods. Computational results show that it produces high quality solution in very short running times when compared to other algorithms.     

基于人工鱼群和微粒群混合算法的WSN节点部署策略

将无线传感器网络节点分布部署问题形式化为一个组合优化问题,以网络覆盖率为目标函数。针对该模型 提出基于人工鱼群与微粒群的混合算法的无线传感器网络节点部署优化策略。微粒群算法搜索效率高,而人工鱼群 算法进行搜索时有很好的全局性。AF SA-POS算法将这两种算法相结合,局部搜索速度快,而且有效地解决了标准 PS<)算法中的粒子“早熟”问题。最后使用MA"I'LAI3进行了实验,结果表明提出的算法减少了迭代次数,并且提高了 网络覆盖率,相对于人工鱼群算法和微粒群算法来说能取得更好的效果。     

An improved particle swarm optimization algorithm for flowshop scheduling problem

The flowshop scheduling problem has been widely studied and many techniques have been applied to it, but few algorithms based on particle swarm optimization (PSO) have been proposed to solve it. In this paper, an improved PSO algorithm (IPSO) based on the “alldifferent” constraint is proposed to solve the flow shop scheduling problem with the objective of minimizing makespan. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a particle is going to stagnate, the mutation operator is used to search its neighborhood. The proposed algorithm is tested on different scale benchmarks and compared with the recently proposed efficient algorithms. The results show that the proposed IPSO algorithm is more effective and better than the other compared algorithms. It can be used to solve large scale flow shop scheduling problem effectively.     

Ensemble particle swarm optimizer

According to the “No Free Lunch (NFL)” theorem, there is no single optimization algorithm to solve every problem effectively and efficiently. Different algorithms possess capabilities for solving different types of optimization problems. It is difficult to predict the best algorithm for every optimization problem. However, the ensemble of different optimization algorithms could be a potential solution and more efficient than using one single algorithm for solving complex problems. Inspired by this, we propose an ensemble of different particle swarm optimization algorithms called the ensemble particle swarm optimizer (EPSO) to solve real-parameter optimization problems. In each generation, a self-adaptive scheme is employed to identify the top algorithms by learning from their previous experiences in generating promising solutions. Consequently, the best-performing algorithm can be determined adaptively for each generation and assigned to individuals in the population. The performance of the proposed ensemble particle swarm optimization algorithm is evaluated using the CEC2005 real-parameter optimization benchmark problems and compared with each individual algorithm and other state-of-the-art optimization algorithms to show the superiority of the proposed ensemble particle swarm optimization (EPSO) algorithm.     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

     

Bacterial Foraging Optimization Approach to Portfolio Optimization

In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.     

一种全局收敛的PSO算法及其收敛分析

在分析了速度因子对微粒群算法影响的基础上,针对以往算法的弱点,提出了一种基于Gaussian变异全局收敛的粒子群算法.该算法使用全局变异因子使粒子具有了良好的全局搜索能力,并证明了它能以概率1收敛到全局最优解.同时使用了局部变异因子,使算法在局部搜索过程中具有较高的搜索精度.典型函数优化的仿真结果表明,该算法具有寻优能力强、搜索精度高、稳定性好等优点,适合于工程应用中的函数优化问题.     

QoS multicast routing using a quantum-behaved particle swarm optimization algorithm

QoS multicast routing in networks is a very important research issue in networks and distributed systems. It is also a challenging and hard problem for high-performance networks of the next generation. Due to its NP-completeness, many heuristic methods have been employed to solve the problem. This paper proposes the modified quantum-behaved particle swarm optimization (QPSO) method for QoS multicast routing. In the proposed method, QoS multicast routing is converted into an integer programming problem with QoS constraints and is solved by the QPSO algorithm combined with loop deletion operation. The QPSO-based routing method, along with the routing algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA), is tested on randomly generated network topologies for the purpose of performance evaluation. The simulation results show the efficiency of the proposed method on QoS the routing problem and its superiority to the methods based on PSO and GA.