基于APSO-BP算法的水库生态优化调度研究

APSO-BP算法可实现粒子群优化的自动调整,解决传统粒子群优化算法收敛性较差,局部易出现极小值的局限,文章引入APSO-BP算法对辽宁某水库生态调度进行优化计算。研究结果表明:APSO-BP算法的收敛精度和全局优化搜索能力都好于传统粒子群优化算法,经APSO-BP算法优化下的水库生态调度可以满足流域的生态蓄水,流域最小生态需水满足度达到75%,适宜生态需水满足度达到66.7%,生态优化调度结果明显好于传统粒子群优化算法。APSO-BP算法可用于水库生态优化调度。研究成果对于水库生态调度方案以及区域生态蓄水规划配置提供方法参考。     

基于混合粒子群法的微电网优化研究

微电网的优化运行是一个具有重要经济和社会效益的复杂问题。文章研究了在微电网中含有多种分布式电源,在并网运行方式下,建立了包含运行成本、微网与主网交互成本以及污染物治理费用的微电网多目标优化调度模型,应用粒子群优化算法对其优化调度模型进行求解,并针对一年中典型负荷日对微电网系统进行分析,验证该算法对微电网环保经济运行的有效性与可行性。     

基于CPSO的基础设施模糊投资组合优化模型

针对基础设施效益模糊、难以度量的特点,结合模糊集理论,建立了模糊投资组合优化模型,改进粒子群算法,加入混沌思想,使用混沌粒子群算法(CPSO)求解基础设施的模糊投资组合优化模型。以4个城市投资公司的数据为样本,验证该方法的科学性与有效性。研究结果表明:模糊投资组合优化模型可较好地表征基础设施的模糊效益,提高基础设施投资决策的科学性;混沌寻优思想改进的粒子群算法可求得模糊投资组合优化模型的全局最优解,增强算法的鲁棒性。     

基于改进型离散粒子群算法变电检修计划优化

建立电网变电检修成本的优化目标数学函数,并针对粒子随机变异思想的改进型离散粒子群算法,建立与模型迭代优化相适应的粒子编码方式,以降低检修的运维成本,提高运行维护工作效率。     

关于主动配电网优化调度的策略研究

主动配电网融传统配网元素、分布式发电、可再生能源、柔性负载等于一体,是智能电网的重要组成部分。要实现主动配电网的综合效益需要对传统调度策略进行变革和优化。文章设计了一种统筹主动配电网各要素特性的优化调度模型,该模型充分考虑分时电价、柔性负载的能量容量约束等对电网运行成本的影响,并以可控分布式发电的调控和网络联络开关的控制作为主要依托,追求单个调度周期内的运行成本最低。为了保证调度策略可求解,文章提出智能单粒子优化算法。算例表明文章构建的主动配电网优化调度模型及其求解算法的有效性。     

基于发电成本与环境效益的风电最佳接入容量分析

为充分了解风电接入电力系统后对发电成本及环境效益的影响,计算符合系统运行经济性的风电接入容量,建立了风电最佳接入容量优化模型。采用该模型分析了不同容量火电机组在不同功率下的煤耗特性变化,并通过对火电机组深度调峰补偿调动火电机组调峰积极性;引入火电排污成本以充分体现风电接入后的环境效益,加入弃风惩罚费用来保障风电优先调度。采用粒子群算法并结合主动搜索技术(active explore basic particle swarm optimization,AEPSO)对模型进行求解,有效解决了粒子群算法易陷入局部最优的问题。最后以某地区实际电力系统为例,计算得出了在最经济运行模式下的风电接入容量,验证了模型的合理性。该研究可为相关决策部门制定风电发展规划提供参考。     

基于粒子群神经网络的软岩巷道变形预测

软岩巷道变形预测在采矿工程中具有十分重要的意义.软岩巷道变形预测是一个典型的多变量、非线性系统.运用粒子群优化的神经网络构造了软岩巷道变形预测的模型,以此训练好的粒子群优化神经网络模型来描述软岩巷道变形和主要影响因素之间的关系.该方法以神经网络为基础,用粒子群算法来优化神经网络,综合利用二者的优点,进行软岩巷道变形预测.实验表明,此方法是高效可行的,并可在更多领域内应用。     

基于SVR-IPSO的高危行业企业安全投入优化模型及其改进——以煤炭生产企业为例

传统的安全投入模型对解决高危行业领域中模糊复杂的安全投入问题具有一定局限性,尤其当建立目标函数时,采用隐含线性关系假设的函数进行拟合会影响模型的推广能力。基于此,本文首先采用支持向量回归机（SVR）建立事故损失模型,与传统C-D函数拟合结果相比,该模型具有更好的预测能力;然后,以实际安全投入要求为约束,以安全总成本最小化为原则建立企业安全投入优化模型;最后,采用基于捕食搜索策略的粒子群算法对模型进行求解,同时,为保证全局收敛性,引入自适应控制策略对算法进行了改进。结果表明：该模型能够更加准确地描述安全投入与安全成本间的非线性作用关系,并通过粒子群寻优得到具备可行性的全局最优解,为高危行业企业安全投入结构优化提供新的决策思路。     

成品油管道调度计划优化研究

以全线泵机组耗费用最小为目标函数,建立成品油管道调度计划自动编制与优化模型。该模型主要采用集中分输方式,考虑管道的水力约束,采用的遗传算法对模型进行优化求解。详细介绍了针对成品油管道调度计划优化问题遗传算法编码的方式,算法各主要算子的选择,适应度函数的建立,惩罚函数的引入等问题。根据所建立的数学模型,利用c语言在Windows7环境下编制了成品油管道调度计划自动编制与优化程序。利用该程序对西南成品油管进行模拟计算,得到优化后各分输站分输各批次油品的起止时间,能耗费用,批次跟踪带状图。模拟计算结果表明计划合理可行,可对调度计划进行优化。     

基于改进粒子群的分布式通信干扰资源分配

为了解决分布式通信干扰场景下面临的资源分配效率低、干扰效益无保障等问题,结合通信干扰资源分配数学模型,设计了一种改进的粒子群算法。首先设计了分布式通信干扰场景并构建了通信干扰资源分配模型,以最大化干扰效益作为目标函数;其次采用自适应惯性因子和学习因子,并引入遗传变异策略和精英保留策略,提出一种改进的粒子群算法,最后对不同场景规模的通信干扰资源分配进行仿真实验。结果表明,相比小生境遗传算法、粒子群算法、遗传算法,改进的粒子群算法在不同场景规模下,均能获得更优的干扰效益,性能方面具备整体干扰效益更高、算法收敛速度更快、算法收敛误差更小等优势。所设计的改进粒子群算法可应用在分布式通信干扰场景中,为指挥决策提供参考。     

A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search

The job-shop scheduling problem is one of the most arduous combinatorial optimization problems. Flexible job-shop problem is an extension of the job-shop problem that allows an operation to be processed by any machine from a given set along different routes. This paper present a new approach based on a hybridization of the particle swarm and local search algorithm to solve the multi-objective flexible job-shop scheduling problem. The particle swarm optimization is a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The multi-objective particle swarm algorithm is applied to the flexible job-shop scheduling problem based on priority. Also the presented approach will be evaluated for their efficiency against the results reported for similar algorithms (weighted summation of objectives and Pareto approaches). The results indicate that the proposed algorithm satisfactorily captures the multi-objective flexible job-shop problem and competes well with similar approaches.     

A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times,sequence-dependent setup times and learning effect

This paper addresses multi-objective (MO) optimization of a single-model assembly line balancing problem (ALBP) where the operation times of tasks are unknown variables and the only known information is the lower and upper bounds for operation time of each task. Three objectives are simultaneously considered as follows: (1) minimizing the cycle time, (2) minimizing the total equipment cost, and (3) minimizing the smoothness index. In order to reflect the real industrial settings adequately, it is assumed that the task time is dependent on worker(s) (or machine(s)) learning for the same or similar activity and sequence-dependent setup time exists between tasks. Finding an optimal solution for this complicated problem especially for large-sized problems in reasonable computational time is cumbersome. Therefore, we propose a new solution method based on the combination of particle swarm optimization (PSO) algorithm with variable neighborhood search (VNS) to solve the problem. The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Comparison with an existing multi-objective evolutionary computation method in the literature shows the superior efficiency of our proposed PSO/VNS algorithm.     

粒子群算法和灰狼算法的融合

为了解决传统粒子群算法(PSO)容易“早熟”、陷入局部最优以及灰狼算法(GWO)收敛速度慢的问题。首先，采用GWO算法的个体极值更新策略来实现个体包围式向最优值趋近，融入PSO算法的速度更新策略来实现群体向最优值的趋近，并且在原始粒子群算法基础上加入线性惯性权重递减来提高算法的收敛速度，从而提出了一种基于灰狼算法和改进的粒子群算法(IPSO)的融合优化算法(GW-IPSO);其次，通过6个经典算例进行仿真试验，将融合算法与PSO算法、IPSD算法、灰狼和粒子群结合算法(GW-PSO)进行对比；最后，应用融合算法对二级直线倒立摆的控制器设计进行参数寻优。结果表明：针对6个标准测试函数，混合算法的30次试验结果平均值更接近最优值，且标准差几乎都是最小的；应用在倒立摆控制问题上，系统在5 s左右进入稳定状态。融合后的GW-IPSO算法能够在一定程度上避免早熟和陷入局部极值的问题发生，并且能够很好地应用于控制器设计过程中参数寻优问题。     

Hybrid particle swarm optimization with mutation for optimizing industrial product lines: An application to a mixed solution space considering both discrete and continuous design variables

This article presents an artificial intelligence-based solution to the problem of product line optimization. More specifically, we apply a new hybrid particle swarm optimization (PSO) approach to design an optimal industrial product line. PSO is a biologically-inspired optimization framework derived from natural intelligence that exploits simple analogues of collective behavior found in nature, such as bird flocking and fish schooling. All existing product line optimization algorithms in the literature have been so far applied to consumer markets and product attributes that range across some discrete values. Our hybrid PSO algorithm searches for an optimal product line in a large design space which consists of both discrete and continuous design variables. The incorporation of a mutation operator to the standard PSO algorithm significantly improves its performance and enables our mechanism to outperform the state of the art Genetic Algorithm in a simulated study with artificial datasets pertaining to industrial cranes. The proposed approach deals with the problem of handling variables that can take any value from a continuous range and utilizes design variables associated with both product attributes and value-added services. The application of the proposed artificial intelligence framework yields important implications for strategic customer relationship and production management in business-to-business markets.     

Particle swarm optimization: A study of particle displacement for solving continuous and combinatorial optimization problems

This article addresses the particle swarm optimization (PSO) method. It is a recent proposed algorithm by Kennedy and Eberhart [1995. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks (Perth, Australia), vol. IV, IEEE Service Center, Piscataway, NJ, pp. 1942–1948]. This optimization method is motivated by social behaviour of organisms such as bird flocking and fish schooling. PSO algorithm is not only a tool for optimization, but also a tool for representing socio-cognition of human and artificial agents, based on principles of social behaviour. Some scientists suggest that knowledge is optimized by social interaction and thinking is not only private but also interpersonal. PSO as an optimization tool, provides a population-based search procedure in which individuals called particles change their position (state) with time. In a PSO system, particles fly in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and according to the experience of neighbours, making use of the best position encountered by itself and its neighbours. In this paper, we propose firstly, an extension of the PSO system that integrates a new displacement of the particles (the balance between the intensification process and the diversification process) and we highlight a relation between the coefficients of update of each dimension velocity between the classical PSO algorithm and the extension. Secondly, we propose an adaptation of this extension of PSO algorithm to solve combinatorial optimization problem with precedence constraints in general and resource-constrained project scheduling problem in particular. The numerical experiments are done on the main continuous functions and on the resource-constrained project scheduling problem (RCPSP) instances provided by the psplib. The results obtained are encouraging and push us into accepting than both PSO algorithm and extensions proposed based on the new particles displacement are a promising direction for research.     

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.     

Maximizing production rate and workload smoothing in assembly lines using particle swarm optimization

Particle swarm optimization (PSO) one of the latest developed population heuristics has rarely been applied in production and operations management (POM) optimization problems. A possible reason for this absence is that, PSO was introduced as global optimizer over continuous spaces, while a large set of POM problems are of combinatorial nature with discrete decision variables. PSO evolves floating-point vectors (called particles) and thus, its application to POM problems whose solutions are usually presented by permutations of integers is not straightforward. This paper presents a novel method based on PSO for the simple assembly line balancing problem (SALBP), a well-known NP-hard POM problem. Two criteria are simultaneously considered for optimization: to maximize the production rate of the line (equivalently to minimize the cycle time), and to maximize the workload smoothing (i.e. to distribute the workload evenly as possible to the workstations of the assembly line). Emphasis is given on seeking a set of diverse Pareto optimal solutions for the bi-criteria SALBP. Extensive experiments carried out on multiple test-beds problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing multi-objective population heuristics show a quite promising higher performance for the proposed approach.