Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effects

This paper presents a chaotic self-adaptive particle swarm optimization algorithm (CSAPSO) to solve dynamic economic dispatch problem (DED) with value-point effects. The proposed algorithm takes PSO as the main evolution method. The velocity, a sensitive parameter of PSO, is adjusted dynamically to increase the precision of PSO. To overcome the drawback of premature in PSO, chaotic local search is imported into proposed algorithm. Moreover, a new strategy is proposed to handle the various constraints of DED problem in this paper, the results solved by proposed strategy can satisfy the constraints of DED problem well. Finally, the high feasibility and effectiveness of proposed CSAPSO algorithm is validated by three test systems consisting of 10 and extended 30 generators while compared with the experimental results calculated by the other methods reported in this literature.     

Tsp-based scheduling in a batch-wise hybrid flow-shop

In the past few years, flexible production systems have allowed an extensive exploitation of new technologies, but have also led to new difficulties in production planning management science. The model presented in this paper extends the traditional HFS (hybrid flow-shop) scheduling problem to the case in which jobs are due to follow strict precedence constraints and batch assignment constraints and the parallel machines at a stage are served by a bottleneck machine. A variant of the well-known TSP problem is used to develop an efficient heuristic solution for the problem. The effectiveness of the proposed approach is validated through a comparison with different heuristics traditionally used in HFS scheduling problems. Furthermore, a simple insertion heuristic based on the TSP model of the problem is tested. Finally, a MIP-based approach is also explored to provide the optimum solutions within much larger times for comparison.     

An algorithmic framework of discrete particle swarm optimization

Particle swarm optimization (PSO) was originally developed for continuous problem. To apply PSO to a discrete problem, the standard arithmetic operators of PSO are required to be redefined over discrete space. In this paper, a concept of distance over discrete solution space is introduced. Under this notion of distance, the PSO operators are redefined. After reinterpreting the composition of velocity of a particle, a general framework of discrete PSO algorithm is proposed. As a case study, we illustrate the application of the proposed discrete PSO algorithm to number partitioning problem (NPP) step by step. Preliminary computational experience is also presented. The successful application shows that the proposed algorithmic framework is feasible.     

一类新颖的粒子群优化算法

粒子群优化(PSO)是一类有效的随机全局优化技术。它利用一个粒子群搜索解空间,每个粒子表示一个被优化问题的解,通过粒子间的相互作用发现复杂搜索空间中的最优区域。提出一类新颖的PSO算法,该算法在基本PSO算法的粒子位置更新公式中增加了一个积分控制项。积分控制项根据每个粒子的适应值决定粒子位置的变化,改善了PSO算法摆脱局部极小点的能力。另外,该算法增加了限制搜索空间范围的机制,这对某些函数优化问题是必需的。用5个基准函数做的对比实验结果显示,该算法优于基本PSO算法以及自适应修改惯性因子的PSO算法。     

A modified particle swarm optimization for production planningproblems in the TFT Array process

In this study, a practical production planning problem in the TFT (thin film transistor) Array process is introduced. Several researchers have referred to the capacitated production lot-sizing allocation problems as NP-Hard. Naturally, it is harder to solve the capacitated production allocation problem considering its practical characteristics and constraints, such as allocation problems among bottleneck machines, photo masks, and products with different re-entrant layers. In response to this, we proposed a novel variation of the particle swarm optimization (PSO) model called the modified PSO (MPSO), which is a binary PSO model with dynamic inertia weight and mutation mechanism. It improves some weaknesses as opposed to the original version of the PSO, including a propensity for obstruction near the optimal solution regions that hardly improve solution quality by fine tuning. In addition, it is converted to be able to solve the model of binary decision variables. In order to illustrate effectiveness, the traditional PSO (TPSO), genetic algorithm (GA), and the proposed MPSO are compared by application of the literature’s well-known test problems as well as the practical production planning problem in the TFT Array process. Based on the results of the investigation, it can be concluded that the proposed MPSO is more effective than the other approaches in terms of superiority of solution and required CPU time.     

复杂约束条件下的混合粒子群优化算法*

针对具有复杂约束条件的优化问题,提出了一种混合粒子群算法。该混合算法在将标准粒子群算法与线性搜索法有机结合的基础上,依次对粒子的每一维变量进行适当变化并同时判断其变化的效果。最后进行了数值实验,其结果表明,所提出的混合粒子群算法对于具有复杂有约束条件的优化问题有较好的优化效果。     

基于改进PSO和FCM的模糊辨识

为了提高T-S模糊模型的辨识精度和效率,本文提出了一种改进的粒子群算法和模糊C均值聚类算法相结合的模糊辨识新方法。在该方法中,针对粒子群算法在处理高维复杂函数时容易陷入局部极值的问题,提出了一种粒子群局部搜索和全局搜索动态调整的全新优化算法。模糊C均值聚类算法是模糊辨识最常用的方法之一,该算法简单,计算效率高,但是对初始化特别敏感,容易陷入局部最优。为了解决这一问题,利用改进粒子群算法的全局搜索能力优化聚类中心,显著地提高了算法的辨识精度和效率。最后,针对非线性系统进行建模仿真,仿真结果表明了本文方法的有效性和优越性。     

Electrochemical machining process parameter optimization using particle swarm optimization

Electrochemical machining (ECM) is a nontraditional process used for the machining of hard materials and metal‐matrix composites. Composites are used in several applications such as aerospace, automobile industries, and medical field. The determination of optimal process parameters is difficult in the ECM process for obtaining maximum material removal rate (MRR) and good surface roughness (SR). In this paper, a multiple regression model is used to obtain the relationship between process parameters and output parameters. Particle swarm optimization (PSO) is one of the optimization techniques for solving the multiobjective problem; it is proposed to optimize the ECM process parameters. Current (C), voltage (V), electrolyte concentration (E), and feed rate (F) are considered as process parameters, and MRR and SR are the output parameters used in the proposed work. The developed multiple regression is statistically analyzed by regression plot and analysis of variance. The optimized value of MRR and SR obtained in PSO is 0.0116 g/min and 2.0106 μm, respectively. Furthermore, PSO is compared with the genetic algorithm. The PSO technique outperforms the genetic algorithm in computation time and statistical analysis. The obtained values are validated to test the significance of the model, and it is noticed that the error value for MRR and SR is within 0.15%.     

Annealing-based particle swarm optimization to solve the redundant reliability problem with multiple component choices

In this paper, the performance of a particle swarm optimization (PSO) algorithm named Annealing-based PSO (APSO) is investigated to solve the redundant reliability problem with multiple component choices (RRP-MCC). This problem aims to choose an optimal combination of components and redundancy levels for a system with a series–parallel configuration that maximizes the overall system reliability. PSO is a population-based meta-heuristic algorithm inspired by the social behavior of the biological swarms that is designed for continuous decision spaces. As a local search engine (LSE), the proposed APSO employs the Metropolis-Hastings strategy, the key idea behind the simulated annealing (SA) algorithm. In APSO, the best position among all particles in each iteration is dynamically improved using the inner loop of the SA (i.e., equilibrium loop) while the temperature is updated in the main loop of the PSO algorithm. The well-known benchmarks are used to verify the performance of the proposed APSO. Even though APSO fails to outperform the best solution obtained in the literature, the contribution of this paper is comprised of the implementation of APSO as a hybrid meta-heuristic as well as the effect of Metropolis-Hastings strategy on the performance of the classical PSO.     

基于粒子群算法的再入式飞行器再入走廊计算方法研究

提出一种利用粒子群算法计算再入式飞行器走廊的方法。从再入式飞行器再入过程的运动方程出发,将连续无限维的再入飞行器走廊上边界计算问题,转化成计算走廊上有限个点的多个最优控制问题,最后利用粒子群寻优解决每个最优控制问题,从而得到可行的走廊上边界曲线,这种方法得到的走廊上界曲线比传统的准平衡滑翔条件估计的上界要高,更能体现RLV的飞行能力。     

An improved self-adaptive PSO technique for short-term hydrothermal scheduling

This paper presents an improved self-adaptive particle swarm optimization algorithm (ISAPSO) to solve hydrothermal scheduling (HS) problem. To overcome the premature convergence of particle swarm optimization (PSO), the evolution direction of each particle is redirected dynamically by adjusting the two sensitive parameters of PSO in the evolution process. Moreover, a new strategy is proposed to handle the various constraints of HS problem in this paper. The results solved by this proposed strategy can strictly satisfy the constraints of HS problem. Finally, the feasibility and effectiveness of proposed ISAPSO algorithm is validated by a test system containing four hydro plants and an equivalent thermal plant. The results demonstrate that the proposed ISAPSO can get a better solution in both robustness and accuracy while compared with the other methods reported in this literature.     

一种基于混沌粒子群优化的OFDM系统资源分配算法

针对无线多用户正交频分复用（OFDM）系统中功率分配问题,提出一种基于效用函数最大化框架的资源分配算法.在实际网络环境中,此类最优化算法为非凸的,利用经典最优化方法很难解决.为此,将智能优化中的粒子群方法应用到非凸优化算法设计中,并针对粒子群优化容易陷入局部极值点的问题,将Logistic混沌搜索嵌入PSO算法中,提出混沌粒子群算法.与同类算法相比,所提出算法不仅有效解决了非凸性问题,而且可以使系统具有更好的性能.     

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.     

参数化运动模型和PSO的自主车运动规划方法

自主地面车辆在障碍物环境下的运动规划问题是一个包含非完整约束条件的全局优化问题。针对该优化问题,提出了一种基于参数化运动模型和改进粒子群优化算法的运动规划方法。该方法将车辆运动模型解耦为参数化弧长-曲率模型和速度模型,并采用混沌映射方法对粒子群优化算法进行了改进,将改进的粒子群优化算法应用于弧长-曲率模型中的参数优化问题。仿真结果证明了该方法的有效性,是自主地面车辆运动规划的一种较好方法。     

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.     

Robust blind multiuser detection based on PSO algorithm in the mismatch environment of receiver spreading codes

This paper deals with robust blind linear minimum mean square error (LMMSE) detection using the particle swarm optimization (PSO) algorithm in the presence of code mismatch. The paper shows that the PSO algorithm incorporating the linear system of the LMMSE detector, which is termed as LPSO, can significantly improve the bit error rate (BER) and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for LPSO. To remedy this problem, a blind LMMSE scheme is proposed and combined with PSO to form a robust blind LPSO (BLPSO) detector under code mismatch scenarios. Several computer simulations are provided to demonstrate the effectiveness of the proposed scheme.     

A particle swarm optimization algorithm for part–machine grouping

Although in the last years different metaheuristic methods have been used to solve the cell formation problem in group technology, this paper presents the first particle swarm optimization (PSO) algorithm designed to address this problem. PSO is a population-based evolutionary computation technique based on a social behavior metaphor. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. A maximum cell size is imposed. Some published exact results have been used as benchmarks to assess the proposed algorithm. The computational results show that the PSO algorithm is able to find the optimal solutions on almost all instances.     

微粒群算法在印染企业车间调度中的研究应用

在对某印染企业的生产状况进行了深入调研和分析的基础上,对流水车间调度、混合流水车间调度和作业车间调度进行了对比研究。同时对微粒群算法进行了深入研究,并根据实际情况对算法进行了部分改动和改进,使之能适用于离散的生产调度问题。最后将改进后的微粒群算法应用到花布印染企业的车间调度中,对加工任务进行优化调度,并实现甘特图的动态生成。论文的结果可直接应用于企业流水车间调度和作业车间调度,具有一定的实际应用价值。     

广义粒子群优化模型

粒子群优化算法提出至今一直未能有效解决的离散及组合优化问题．针对这个问题,文中首先回顾了粒子群优化算法在整数规划问题的应用以及该算法的二进制离散优化模型,并分析了其缺陷．然后,基于传统算法的速度一位移更新操作,在分析粒子群优化机理的基础上提出了广义粒子群优化模型（GPSO）,使其适用于解决离散及组合优化问题．GPSO模型本质仍然符合粒子群优化机理,但是其粒子更新策略既可根据优化问题的特点设计,也可实现与已有方法的融合．该文以旅行商问题（TSP）为例,针对遗传算法（GA）解决该问题的成功经验,使用遗传操作作为GPSO模型中的更新算子,进一步提出基于遗传操作的粒子群优化模型,并以Inverover算子作为模型中具体的遗传操作设计了基于GPSO模型的TSP算法．与采用相同遗传操作的GA比较,基于GPSO模型的算法解的质量与收敛稳定性提高,同时计算费用显著降低．     

基于微粒群与混合蛙跳融合的群体智能算法

针对微粒群算法和混合蛙跳算法存在的早熟收敛问题,提出一种基于微粒群与混合蛙跳算法融合的群体智能算法。新算法将整个群体分成数目相等的蛙群和微粒群群体。在两群体独立进化过程中,设计了一种两群之间的信息替换策略：比较蛙群与微粒群的最佳适应值,如果蛙群进化较好,利用蛙群各子群中最差个体替换微粒群一部分较好个体;否则,用微粒群中较好的一部分个体替换蛙群各子群的最好个体。同时,设计了一种两群之间的相互协作方式。为避免微粒群因早熟收敛而影响信息替换策略效果,适时对其所有个体最好位置进行随机扰动。仿真实验表明,新算法可以有效提高全局搜索能力及收敛速度,对于高维复杂函数问题,算法具有很好的稳定性。