A discrete version of particle swarm optimization for flowshop scheduling problems

Particle swarm optimization (PSO) is a novel metaheuristic inspired by the flocking behavior of birds. The applications of PSO to scheduling problems are extremely few. In this paper, we present a PSO algorithm, extended from discrete PSO, for flowshop scheduling. In the proposed algorithm, the particle and the velocity are redefined, and an efficient approach is developed to move a particle to the new sequence. To verify the proposed PSO algorithm, comparisons with a continuous PSO algorithm and two genetic algorithms are made. Computational results show that the proposed PSO algorithm is very competitive. Furthermore, we incorporate a local search scheme into the proposed algorithm, called PSO-LS. Computational results show that the local search can be really guided by PSO in our approach. Also, PSO-LS performs well in flowshop scheduling with total flow time criterion, but it requires more computation times.     

A multi-stage particle swarm for optimum design of truss structures

The contribution of this study is to propose a multi-stage particle swarm optimization (MSPSO) for structural optimization. In this paper, three auxiliary improving mechanisms are added to the standard particle swarm optimization (PSO) in order to enhance its efficiency and reliability dealing with optimum design of truss structures. These mechanisms effectively accelerate the convergence rate of the PSO and also make it robust to attain better optimum solutions during various runs of the algorithm. The effectiveness of the MSPSO is illustrated by several benchmark structural optimization problems. Results demonstrate the efficiency and robustness of the proposed MSPSO algorithm compared to the standard version of the PSO.     

A PSO-based intelligent decision algorithm for VLSI floorplanning

Floorplanning is an important issue in the very large-scale integrated (VLSI) circuit design automation as it determines the performance, size, yield and reliability of VLSI chips. This paper proposes a novel intelligent decision algorithm based on the particle swarm optimization (PSO) technique to obtain a feasible floorplanning in VLSI circuit physical placement. The PSO was applied with integer coding based on module number and a new recommended value of acceleration coefficients for optimal placement solution. Inspired by the physics of genetic algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed PSO algorithm to make this algorithm to break away from local optima and achieve a better diversity. Experiments employing MCNC and GSRC benchmarks show that the proposed algorithm is effective. The proposed algorithm can avoid local minimum and performs well in convergence. The experimental results of the proposed method in this paper can also greatly help floorplanning decision making in VLSI circuit design automation.     

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. The implementation is validated by utilizing the PSO optimizer as a driver for a single discipline optimization and for a multicriterion optimization and comparing the results to a commercially available gradient based optimization algorithm, previously published results, and a simple sequential Monte Carlo model. A typical conceptual ship design statement from the literature is employed for developing the single discipline and the multicriterion benchmark optimization statements. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. Results are compared to MDO analyses performed when a gradient based optimizer comprised the optimization driver at all levels. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.     

Cooperative bare-bone particle swarm optimization for data clustering

Cooperative coevolution (CC) was used to improve the performance of evolutionary algorithms (EAs) on complex optimization problems in a divide-and-conquer way. In this paper, we show that the CC framework can be very helpful to improve the performance of particle swarm optimization (PSO) on clustering high-dimensional datasets. Based on CC framework, the original partitional clustering problem is first decomposed to several subproblems, each of which is then evolved by an optimizer independently. We employ a very simple but efficient optimization algorithm, namely bare-bone particle swarm optimization (BPSO), as the optimizer to solve each subproblem cooperatively. In addition, we design a new centroid-based encoding schema for each particle and apply the Chernoff bounds to decide a proper population size. The experimental results on synthetic and real-life datasets illustrate the effectiveness and efficiency of the BPSO and CC framework. The comparisons show the proposed algorithm significantly outperforms five EA-based clustering algorithms, i.e., PSO, SRPSO, ACO, ABC and DE, and K-means on most of the datasets.     

基于文化微粒群算法的丙烯腈收率软测量模型

丙烯腈收率是丙烯腈装置的关键指标,如何得到丙烯腈收率是厂家很关注的研究,将新型优化算法用于丙烯腈收率软测量建模是1种较好的尝试。将新型微粒群优化算法用于同样新型的文化算法种群空间的优化,设计文化微粒群优化算法。它由种群空间和信念空间2部分组成,在种群空间和信念空间分别采用各自算法并行演化,同时,2个空间又根据一定的协议相互联系。分别将该算法和基本微粒群算法用于一些常用测试函数的优化问题;结果表明,与基本微粒群算法相比,文化微粒群算法加强了全局搜索能力,更容易收敛于全局最优解。最后将文化微粒群优化算法用于优化神经网络,构成文化微粒群神经网络,并将其应用于丙烯腈收率软测量建模。结果表明,此模型精度高,应用前景广阔。     

求解工程约束优化问题的PSO-ABC混合算法*

针对包含约束条件的工程优化问题,提出了基于人工蜂群的粒子群优化PSO-ABC算法。将PSO中较优的粒子作为ABC算法的蜜源,并使用禁忌表存储其局部极值,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性规则进行约束处理,将粒子种群分为可行子群和不可行子群,并在ABC算法产生蜜源的过程中保留部分较优的可行解和不可行解的信息,弥补了可行性规则处理最优点位于约束边界附近的问题时存在的不足。四个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。     

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.     

A new PSO-based algorithm for multi-objective optimization with continuous and discrete design variables

This paper presents a new multi-objective optimization algorithm called FC-MOPSO for optimal design of engineering problems with a small number of function evaluations. The proposed algorithm expands the main idea of the single-objective particle swarm optimization (PSO) algorithm to deal with constrained and unconstrained multi-objective problems (MOPs). FC-MOPSO employs an effective procedure in selection of the leader for each particle to ensure both diversity and fast convergence. Fifteen benchmark problems with continuous design variables are used to validate the performance of the proposed algorithm. Finally, a modified version of FC-MOPSO is introduced for handling discrete optimization problems. Its performance is demonstrated by optimizing five space truss structures. It is shown that the FC-MOPSO can effectively find acceptable approximations of Pareto fronts for structural MOPs within very limited number of function evaluations.     

A hybrid biogeography-based optimization algorithm for job shop scheduling problem

In this paper, a hybrid biogeography-based optimization (HBBO) algorithm has been proposed for the job-shop scheduling problem (JSP). Biogeography-based optimization (BBO) is a new bio-inpired computation method that is based on the science of biogeography. The BBO algorithm searches for the global optimum mainly through two main steps: migration and mutation. As JSP is one of the most difficult combinational optimization problems, the original BBO algorithm cannot handle it very well, especially for instances with larger size. The proposed HBBO algorithm combines the chaos theory and “searching around the optimum” strategy with the basic BBO, which makes it converge to global optimum solution faster and more stably. Series of comparative experiments with particle swarm optimization (PSO), basic BBO, the CPLEX and 14 other competitive algorithms are conducted, and the results show that our proposed HBBO algorithm outperforms the other state-of-the-art algorithms, such as genetic algorithm (GA), simulated annealing (SA), the PSO and the basic BBO.     

基于粒子群优化算法的PID控制器参数整定

PID控制器的性能完全依赖于其参数的整定和优化,但参数的整定及在线自适应调整对常规的PID控制器是难以解决的问题。根据粒子群算法具有对整个参数空间进行高效并行搜索的特点,提出了一种基于粒子群优化算法整定PID控制器参数的设计方法,并定义了一种新的性能指标函数来评价PID控制器的性能。现以二阶的船舶控制装置为研究对象,运用粒子群优化方法对PID控制器参数进行了寻优研究。仿真结果表明,该方法比一般PID参数整定方法具有更好的控制性能指标,有着一定的工程应用价值。     

A fuzzy particle swarm optimization algorithm for computer communication network topology design

Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem.     

基于AKPSO算法的加速度计快速标定方法

针对粒子群优化（ PSO）算法在加速度计标定优化后期出现的早熟、陷入局部最优的不足,以及KalmanPSO（ KPSO）算法在设计与应用过程中存在的缺陷,提出了基于自适应 Kalman 滤波的改进 PSO （ AKPSO）算法,并将其成功应用于加速度计快速标定。利用粒子群状态空间Markov链模型,建立了粒子群系统状态方程和观测方程;采用指数加权的自适应衰减记忆Kalman滤波来对粒子的位置进行估计。加速度计标定仿真结果表明：所提出的算法在收敛速度、收敛精度方面都要优于PSO,KPSO算法,有效地提高了加速度计的标定精度。     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

基于改进PSO算法的LSSVM入侵检测模型

在基本PSO算法和线性权重下降PSO算法的基础上,提出一种并行PSO算法,将粒子群分成两组,分别采用不同的惯性权重,各侧重于全局搜索和局部搜索,根据进化代数动态调整两种算法中进化的粒子数。通过仿真实验,证明了并行PSO算法的寻优性能优于基本PSO算法和线性权重下降PSO算法。     

Design of two-channel quadrature mirror filter bank using particle swarm optimization

In this paper, a particle swarm optimization (PSO) is used to design the quadrature mirror filter (QMF) banks with linear phase in frequency domain. A unique PSO is developed to optimize filter bank coefficients to match the ideal system response. The three main attributes used in assessing the performance of filter are reconstruction error, mean square error in pass band and mean square error in stop band. The proposed PSO based method produces almost the same result as that of the existing methods. As compared to the existing methods this method is very simple to implement for the QMF bank optimization. To implement the proposed PSO algorithm, a Matlab program is developed and three examples have been presented to illustrate the performance of the proposed method.     

A multi-objective mixed-discrete particle swarm optimization with multi-domain diversity preservation

Among population-based optimization algorithms guided by meta-heuristics, Particle Swarm Optimization (PSO) has gained significant popularity in the past two decades, particularly due to its ease of implementation and fast convergence capabilities. This paper seeks to translate the beneficial features of PSO from solving typical continuous single-objective problems to solving multi-objective mixed-discrete problems, which is relatively a new ground for PSO application. The original Mixed-Discrete PSO (MDPSO) algorithm, which included an exclusive diversity preservation technique to significantly mitigate premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. This papers makes fundamental advancements to MDPSO, enabling it to solve complex multi-objective problems with mixed-discrete design variables. Specifically, in the velocity update equation for any particle, the explorative term is modified to point towards a stochastically selected non-dominated solution at that iteration ? thereby adopting the concept of multi-leader swarms. The fractional domain in the diversity preservation technique, which was previously defined in terms of the best global particle, is now formulated as a function of the extreme members in the set of intermediate Pareto optimal solutions. With this advancement, diversity preservation not only mitigates premature particle stagnation, but also promotes more uniform coverage of the Pareto frontier. The multi-objective MDPSO algorithm is tested using a set of benchmark problems and a wind farm layout optimization problem. To illustrate the competitive benefits of the new MO-MDPSO algorithm, the results are compared with those given by other popular multi-objective solvers such as NSGA-II and SPEA.     

基于粒子群优化算法的神经网络在英文字母识别中的应用

粒子群优化算法是基于群体智能理论的全局优化算法,它通过种群中粒子间的合作与竞争,对解空间进行智能搜索,从而发现最优解。其优势在于操作简单,容易实现。文中对一种新的算法PSO-BP进行了研究,该充分发挥了粒子群算法的全局寻优能力和BP算法的局部搜索优势,并与BP算法一起对44像素的英文字母进行识别比较。实验表明,将粒子群算法用于神经网络的优化,收敛速度更快,预测精度更高,而且算法简单,且对更高像素的英文字母识别同样适用。     

QPSO算法优化的非线性观测器设计方法研究

具有量子行为的粒子群优化算法(Quantum-behavedParticleSwarmOptimization,简称QPSO)是继粒子群优化算法(ParticleSwarmOptimization,简称PSO)后,最新提出的一种新型、高效的进化算法。论文在研究基于PSO算法的非线性观测器基础上,提出了一种基于QPSO算法的非线性观测设计方法。以vanderPol系统为例进行了仿真实验,其基本思想是将非线性连续时间系统的状态估计问题转换为非线性函数的在线优化问题,然后利用PSO或QPSO算法获得系统状态的最优估计。仿真结果显示了基于QPSO算法的非观测器比基于PSO算法的非线性观测器的性能更优越。     

嵌入局部一维搜索技术的混合粒子群优化算法*

通过将粒子群优化算法(PSO)与经典局部一维搜索技术相结合,提出一种嵌入局部一维搜索技术的混合粒子群优化算法(LLS-PSO)。该算法在基本粒子群优化算法中引入一维搜索技术,选取最优粒子进行局部一维搜索,增强了在最优点附近的局部搜索能力,以加快算法的收敛速度。对三个经典复杂优化问题进行数值实验,并与基本PSO算法进行比较。实验分析和结果表明,LLS-PSO具有更好的优化性能。