面向柔性作业车间调度的多策略鲸鱼优化算法

以某大型家具企业的柔性生产制造过程中调度问题为研究对象,提出了一种主要用于求解柔性作业车间调度问题的多策略鲸鱼优化算法(multi-strategy whale optimization algorithm, MWOA),首先,为了提高初始种群的多样性,引入混沌理论来初始化种群;同时设计了非线性收敛因子和自适应惯性权重系数来平衡全局探索和局部开发能力;然后结合差分进化(differential evolution, DE)算子提高了WOA的利用和搜索能力,最后采取最优个体混沌搜索策略,减少WOA算法出现早熟收敛现象的概率.以最小化最大完工时间为求解目标,对基准测试问题与某家具企业的生产制造过程的调度优化问题进行了求解,结果表明提出来的多策略鲸鱼优化算法克服了基本鲸鱼优化算法寻优精度低、收敛速度慢及容易陷入局部最优等缺陷,与对比算法比较,取得了更好的寻优效果.     

差异工件平行机批调度问题的SAGA*

为了求解差异工件平行机批调度问题,提出了一种模拟退火遗传算法 (simulated annealing genetic algorithm,SAGA)。将模拟退火算法（simulated annealing,SA）的状态转移操作引入基于最优保留的遗传算法(genetic algorithm,GA)中,作为局部搜索算子,以避免算法陷入局部最优,也有效地发挥了SA和GA在局部搜索与全局搜索能力方面的优势。为了解决GA迭代后期适应函数难以区分一些适应度接近的个体这个问题,SAGA分两阶段标定适应函数,在进化后期     

基于总空闲时间增量的无等待流水调度混合遗传算法

将NP-难的最小化最大完工时间无等待流水调度问题等价转化为最小化总空闲时间的问题,改变传统求解调度序列目标函数的模式,通过目标函数变化量判断新解的优劣,大大降低算法所需计算时间.分析启发式算法基本操作和进化算子的总空闲时间增量性质,设计基本总空闲时间增量法以快速评估新产生解的质量.提出混合遗传算法IHGA(increment based hybrid genetic algorithm)求解该问题,构造相应初始种群生成方法和进化算子,提出进化概率动态更新策略和种群收敛判断与再生机制;算法混合了迭代改进局部搜索以进一步提高解的质量,基于120个经典Benchmark实例,将IHGA与目前求解该问题的有效算法RAJ,GR,SA2,TSM和FCH进行比较,实验结果表明:IHGA在性能方面优于其他,计算效率方面优于SA2和TSM,略逊于GR,RAJ和FCH.     

带工序跳跃的绿色混合流水车间机器与AGV联合调度

针对带工序跳跃的绿色混合流水车间机器和自动引导车(AGV)联合调度问题,提出改进memetic algorithm (MA)以同时最小化最大完工时间和总能耗.首先,设计基于工序、机器和转速的三层编码策略,最大程度保证算法在整个解空间中搜索;然后,设计混合种群初始化方法以提高初始种群解的质量,同时设计交叉和变异算子以及两种基于问题的邻域搜索策略来平衡算法的全局搜索和局部搜索能力;最后,通过大量仿真实验验证MA算法求解该问题的有效性和优越性.     

改进灰狼优化算法求解模糊车间调度问题

模糊车间调度问题是复杂调度的经典体现,针对此问题设计优秀的调度方案能提高生产效率。目前对于模糊车间调度问题的研究主要集中在单目标上,因此提出一种改进的灰狼优化算法（improved grey wolf optimization,IGWO）求解以最小化模糊完成时间和最小化模糊机器总负载的双目标模糊柔性作业车间调度问题。该算法首先采用双层编码将IGWO离散化,设计一种基于HV贡献度的策略提高种群多样性;然后使用强化学习方法确定全局和局部的搜索参数,改进两种交叉算子协助个体在不同更新模式下的进化;接着使用两级变邻域和四种替换策略提高局部搜索能力;最后在多个测例上进行多组实验分析验证改进策略的有效性。在多数测例上,IGWO的性能要优于对比算法,具有良好的收敛性和分布性。     

混合多目标算法用于柔性作业车间调度问题

在生产调度领域,柔性作业车间调度问题是一个非常重要的优化问题。大多数研究通常优化的目标只是最大完工时间,而在实际中,往往要考虑多个目标。因此,提出了一种新的混合多目标算法用于解决柔性作业车间调度问题,其中考虑了3个目标,分别是:最大完工时间、机器总负载和瓶颈机器负荷。算法设计了有效的编码方式和遗传算子,并采用非支配近邻免疫算法求解非支配最优解。为了提高算法性能,提出了3种不同的局部搜索策略,并将其结合在多目标算法中。在多个数据集上的实验对比结果表明,所提算法优于其它代表性的算法。此外,实验结果还验证了局部搜索技术的有效性。     

基于目标增量的无等待流水调度快速迭代贪婪算法

最小化总完工时间无等待流水调度是典型的NP-完全问题,广泛存在于实际生产系统.改变传统求解调度序列目标函数的模式,提出目标增量法,通过目标函数变化量判断新解的优劣,大大降低算法所需计算时间;通过证明启发式算法基本操作的目标增量性质,设计两种基本目标增量法以快速评估新产生解的质量.提出快速迭代贪婪算法FIG(Fast Iterative Greedy algorithm)求解该问题,构造初始解生成算法,提出分段式重构局部搜索方法和迭代改进全局搜索策略以进一步提高解的质量.基于110个经典Benchmark实例,将提出的FIG算法与目前求解该问题较好的启发式算法PHlp和元启发式算法SRTS、DPSOvnd进行比较,实验结果表明FIG在性能上优于SRTS和PHlp,略逊于DPSOvnd;在效率上优于SRTS和DPSOvnd,略逊于PHlp.     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。     

一种多尺度协同变异的萤火虫粒子群混合算法及在车间调度中的应用

车间调度对于制造企业提高生产效率、降低生产成本具有重要的作用,针对单一优化算法在解决调度优化问题时存在的不足,探索求解速度和求解质量的均衡,提出了一种多尺度协同变异的萤火虫粒子群混合算法;引入动态自适应策略把种群分为两组,对两组族群平行进化,在保持种群多样性的同时提高求解速度;引入多尺度协同变异算子,利用不同大小方差的自适应高斯变异机制使种群以尽量分散的变异尺度来搜索解空间,通过混沌初始化种群进一步提高算法的局部检索能力;将提出的算法应用于函数优化和流水车间调度问题求解,实验结果显示,算法在求解效率、精度方面优于对比算法,具有较好的性能和应用价值。     

改进多目标进化算法求解柔性作业车间调度问题

针对加工时间为模糊数的柔性作业车间调度问题,考虑最小化模糊最大完工时间、模糊机器总负荷、模糊关键机器负荷为优化目标,提出一种有效求解该类优化问题的多目标进化算法。算法采用一种混合不同机器分配和工序排序策略的方法产生初始种群,并采用插入空隙法对染色体进行解码。定义一种新的基于可能度的个体支配关系和一种基于决策空间的拥挤算子,并将所提支配关系和拥挤算子运用于快速非支配排序。接着,提出一种基于移动模糊关键工序的局部搜索策略对种群中的优势个体进行局部搜索。通过试验研究关键参数对算法性能的影响并将所提算法与3种不同的优化算法作对比。结果表明,所提算法能够比其它算法更有效解决多目标模糊柔性作业车间调度优化问题。     

Quantum-behaved bat algorithm for many-objective combined economic emission dispatch problem using cubic criterion function

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO₂, NO_x, and CO₂ are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem.

     

Recurrent neural network for combined economic and emission dispatch

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.     

Nonconvex economic load dispatch using an efficient real-coded genetic algorithm

An efficient optimisation procedure based on real-coded genetic algorithm (RCGA) is proposed for the solution of economic load dispatch (ELD) problem with continuous and nonsmooth/nonconvex cost function and with various constraints being considered. The effectiveness of the proposed algorithm has been demonstrated on different systems considering the transmission losses and valve point loading effect in thermal units. The proposed algorithm is equipped with an effective constraint handling technique, which eliminates the need for penalty parameters. For the purpose of comparison, the same problem has also been solved using binary-coded genetic algorithm (BCGA) and three other popular RCGAs. In the proposed RCGA, simulated binary crossover and polynomial mutation are used against the single point crossover and bit-flipping mutation in BCGA. It has been observed from the test results that the proposed RCGA is more efficient in terms of thermal cost minimisation and execution time for ELD problem with continuous search space than BCGA and some other popular RCGAs.     

Tournament-based harmony search algorithm for non-convex economic load dispatch problem

This paper proposes a tournament-based harmony search (THS) algorithm for economic load dispatch (ELD) problem. The THS is an efficient modified version of the harmony search (HS) algorithm where the random selection process in the memory consideration operator is replaced by the tournament selection process to activate the natural selection of the survival-of-the-fittest principle and thus improve the convergence properties of HS. The performance THS is evaluated with ELD problem using five different test systems: 3-units generator system; two versions of 13-units generator system; 40-units generator system; and large-scaled 80-units generator system. The effect of tournament size (t) on the performance of THS is studied. A comparative evaluation between THS and other existing methods reported in the literature are carried out. The simulation results show that the THS algorithm is capable of achieving better quality solutions than many of the well-popular optimization methods.     

An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm

Microgrid is a novel small-scale system of the centralized electricity for a small-scale community such as villages and commercial area. Microgrid consists of micro-sources like distribution generator, solar and wind units. A microgrid is consummate specific purposes like reliability, cost reduction, emission reduction, efficiency improvement, use of renewable sources and continuous energy source. In the microgrid, the Energy Management System is having a problem of Economic Load Dispatch (ELD) and Combined Economic Emission Dispatch (CEED) and it is optimized by meta-heuristic techniques. The key objective of this paper is to solve the Combined Economic Emission Dispatch (CEED) problem to obtain optimal system cost. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The newly introduced Interior Search Algorithm (ISA) is applied for the solution of ELD and CEED problem. The minimization of total cost and total emission is obtained for four different scenarios like all sources included all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of ISA with the Reduced Gradient Method (RGM), Ant Colony Optimization (ACO) technique and Cuckoo Search Algorithm (CSA) for the two different cases which are ELD without emission and CEED with emission. The results are calculated for different Power Demand of 24 h. The results obtained to ISA give comparatively better cost reduction as compared with RGM, ACO and CSA which shows the effectiveness of the given algorithm.

     

Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm

This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.     

基于混合蛙跳算法的电力系统经济负荷分配

针对带阀点效应的经济负荷分配(ELD)问题高维、非凸、非线性的特点,应用混合蛙跳算法(SF-LA)解决电力系统ELD问题。该算法结合了模因演算算法(MA)和粒子群优化(PSO)算法二者的优点,在确保全局收敛和满足约束条件下,能够快速有效地搜索到最优解。通过对多个ELD问题实例进行仿真计算,并与参考文献做比较,结果表明:SFLA对于解决电力系统ELD问题是有效、可行的。     

A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems

This paper presents the design and application of an efficient hybrid heuristic search method to solve the practical economic dispatch problem considering many nonlinear characteristics of power generators, and their operational constraints, such as transmission losses, valve-point effects, multi-fuel options, prohibited operating zones, ramp rate limits and spinning reserve. These practical operation constraints which can usually be found at the same time in realistic power system operations make the economic load dispatch problem a nonsmooth optimization problem having complex and nonconvex features with heavy equality and inequality constraints.The proposed approach combines in the most effective way the properties of two of the most popular evolutionary optimization techniques now in use for power system optimization, the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. To improve the global optimization property of DE, the PSO procedure is integrated as additional mutation operator.The effectiveness of the proposed algorithm (termed DEPSO) is demonstrated by solving four kinds of ELD problems with nonsmooth and nonconvex solution spaces. The comparative results with some of the most recently published methods confirm the effectiveness of the proposed strategy to find accurate and feasible optimal solutions for practical ELD problems.     

A hybrid non-linear time-varying double-weighted particle swarm optimization for solving non-convex combined environmental economic dispatch problem

Fossil-fuel based power sources cause environmental pollution such as the degradation of air quality and climate change, which negatively impacts the life on the earth. Consequently, this demands that the power generation should consider the optimal management of thermal sources that are aimed at minimizing the emission of gasses in the generation mix. The production volume of multi-pollutant gasses (SO₂, NO_x, and CO₂) can be reduced through a combined environmental economic dispatch (CEED) approach. This study has proposed a hybrid algorithm based on a novel combination of a modified genetic algorithm and an improved version of particle swarm optimization abbreviated as MGAIPSO to solve CEED problem. The study utilizes three robust operators to enhance the performance of the proposed hybrid algorithm. In GA, a uniformly weighted arithmetic crossover and a normally distributed mutation operator have been implemented to produce elite off-springs in each iteration and diversify the solutions in the search space. In the case of PSO, a non-linear time-varying double-weighted (NLTVDW) technique is developed to obtain a substantial balance between exploration and exploitation. To further enhance the exploitation ability of the MGAIPSO, this study has implemented two movements correctional methods to continuously monitor and amend the position and velocity of the particles. Several numerical case studies ranging from small to large-scale are carried out to validate the practicality of the proposed algorithm.     

求解电力系统经济调度问题的改进粒子群优化算法

电力系统经济调度问题是电力系统中的一个重要的研究课题,针对该问题,提出一种改进粒子群优化(ODPSO)算法.改进算法在搜索前期,采用广义的反向学习策略,使算法能够快速地靠近较优的搜索区域,从而提高收敛速度;在搜索后期,借鉴差分进化算法的进化机制设计改进的变异和交叉策略,对当前种群的最优粒子进行更新,从而提高种群的多样性,进而协助算法获得全局最优解.为了验证改进粒子群优化算法的有效性,对CEC2006提出的22个基准约束测试函数进行仿真,结果表明改进算法相比其他算法在寻优精度和稳定性上更具优势.最后,将改进算法应用于考虑机组爬坡速率约束、机组禁行区域约束以及电力平衡约束的两个电力系统经济调度问题,取得了令人满意的结果.