A generic optimizer interface for programming-free optimization systems

In this paper, a new mechanism called the generic optimizer interface is presented to bridge different optimization problems with various optimizers for programming-free optimization systems. With this generic interface, an optimization problem is given independent of optimizers and can be solved by different optimizers linked to the interface. Several types of function representations for the generic interface are developed and analyzed, and the indexed binary tree representation (IBTR) is identified to be both efficient and convenient. Different formulations for solving multi-objective optimization problems are then implemented with high computational efficiency by taking advantage of the IBTR. The generic interface is object-oriented and can be easily extended to add new optimizers and formulations.     

多目标微粒群优化算法综述

作为一种有效的多目标优化工具,微粒群优化(PSO)算法已经得到广泛研究与认可.首先对多目标优化问题进行了形式化描述,介绍了微粒群优化算法与遗传算法的区别,并将多目标微粒群优化算法(MOPSO)分为以下几类:聚集函数法、基于目标函数排序法、子群法、基于Pareto支配算法和其他方法,分析了各类算法的主要思想、特点及其代表性算法.其次,针对非支配解的选择、外部档案集的修剪、解集多样性的保持以及微粒个体历史最优解和群体最优解的选取等热点问题进行了论述,并在此基础上对各类典型算法进行了比较.最后,根据当前MOPSO算法的研究状况,提出了该领域的发展方向.     

A modified consensus algorithm for multi-UAV formations based on pigeon-inspired optimization with a slow diving strategy

This paper considers the formation control problem for a group of unmanned aerial vehicles（UAVs） employing consensus with different optimizers.A group of UAVs can never accomplish difficult tasks without formation because if disordered they do not work any better than a single vehicle,and a single vehicle is limited by its undeveloped intelligence and insufficient load.Among the many formation methods,consensus has attracted much attention because of its effectiveness and simplicity.However,at the beginning of convergence,overshoot and oscillation are universal because of the limitation of communication and a lack of forecasting,which are inborn shortcomings of consensus.It is natural to modify this method with lots of optimizers.In order to reduce overshoot and smooth trajectories,this paper first adopted particle swarm optimization（PSO）,then pigeon-inspired optimization（PIO） to modify the consensus.PSO is a very popular optimizer,while PIO is a new method,both work but still retain disadvantages such as residual oscillation.As a result,it was necessary to modify PIO,and a pigeon-inspired optimization with a slow diving strategy（SD-PIO） is proposed.Convergence analysis was performed on the SD-PIO based on the Banach fixed-point theorem and conditions sufficient for stability were achieved.Finally,a series of comparative simulations were conducted to verify the feasibility and effectiveness of the proposed approach.     

一种改进的粒子群优化RBF网络学习算法

提出了一种新的用粒子群优化RBF网络学习的算法,即分组训练合成优化。该算法利用粒子之间的合作与竞争以实现对多维复杂空间的高维搜索能力,找出神经网络权值的最优解,以达到优化神经网络学习的目的。通过与用最小二乘法优化的神经网络进行了比较,结果表明算法所优化的神经网络收敛效果明显、收敛速度快。     

A hybrid particle swarm optimization algorithm for high-dimensional problems

In recent years, particle swarm optimization (PSO) emerges as a new optimization scheme that has attracted substantial research interest due to its simplicity and efficiency. However, when applied to high-dimensional problems, PSO suffers from premature convergence problem which results in a low optimization precision or even failure. To remedy this fault, this paper proposes a novel memetic PSO (CGPSO) algorithm which combines the canonical PSO with a Chaotic and Gaussian local search procedure. In the initial evolution phase, CGPSO explores a wide search space that helps avoid premature convergence through Chaotic local search. Then in the following run phase, CGPSO refines the solutions through Gaussian optimization. To evaluate the effectiveness and efficiency of the CGPSO algorithm, thirteen high dimensional non-linear scalable benchmark functions were examined. Results show that, compared to the standard PSO, CGPSO is more effective, faster to converge, and less sensitive to the function dimensions. The CGPSO was also compared with two PSO variants, CPSO-H, DMS-L-PSO, and two memetic optimizers, DEachSPX and MA-S2. CGPSO is able to generate a better, or at least comparable, performance in terms of optimization accuracy. So it can be safely concluded that the proposed CGPSO is an efficient optimization scheme for solving high-dimensional problems.     

冷热电联供系统多目标运行优化

冷热电联供系统能够提高能源利用率和减少碳排放,是解决能源和环境危机的重要途径.本文提出了冷热电联供系统多目标优化运行方法,以运行成本日节约率、一次能源日节约率、CO2日减排率综合最优为目标,利用遗传算法求解,得到关键设备的逐时出力计划,并以此为基础,设计了由MATLAB和LABVIEW构成的运行优化器.最后本文基于TRNSYS与LABVIEW搭建了冷热电联供系统(CCHP)软硬件混合实时仿真系统,验证了运行优化器的有效性,结果表明本文提出的优化运行方法较传统运行模式,可有效提高冷热电联供系统的经济性、节能性和环保性.     

AlphaQO:鲁棒的学习型查询优化器

由深度学习驱动的学习型查询优化器正在越来越广泛地受到研究者的关注,这些优化器往往能够取得近似甚至超过传统商业优化器的性能.与传统优化器不同的是,一个成功的学习型优化器往往依赖于足够多的高质量的负载查询作为训练数据.低质量的训练查询会导致学习型优化器在未来的查询上失效.提出了基于强化学习的鲁棒的学习型查询优化器训练框架A...     

AlphaQO: Robust Learned Query Optimizer

Recently, learned query optimizers typically driven by deep learning models have attracted wide attention as they can offer similar or even better performance than state-of-the-art commercial optimizers. A successful learning optimizer often relies on enough high-quality load queries as training data, and poor-quality training will lead to the query failure of learned query optimizers. In this paper, we propose a novel training framework AlphaQO for robust learned query optimizers based on Reinforcement Learning (RL), and the robustness of the optimizers can be improved by finding the bad queries in advance. AlphaQO is a loop system consisting of two main components, namely the query generator and the learned optimizer. A query generator aims at generating ``difficult'' queries (i.e., queries that the learned optimizer provides poor estimates). The learned optimizer will be trained using these generated queries, as well as providing feedback (in terms of numerical rewards) to the query generator for updates. If the generated queries are good, the query generator will get a high reward; otherwise, the query generator will get a low reward. The above process is performed iteratively, with the main goal that within a small budget, the learned optimizer can be trained and generalized well to a wide range of unseen queries. Extensive experiments show that AlphaQO can generate a relatively small number of queries and train a learned optimizer to outperform commercial optimizers. Moreover, learned optimizers require much fewer queries from AlphaQO than randomly generated queries for the quality training of the learned optimizer.     

A Cooperative approach to particle swarm optimization

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using multiple swarms to optimize different components of the solution vector cooperatively. Application of the new PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional PSO.     

基于改进型灰狼算法的RFID网络规划

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案.RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题.群体智能(Swarm In-telligence,SI)算法在解决多目标优化问题方面...     

The landscape adaptive particle swarm optimizer

Several modified particle swarm optimizers are proposed in this paper. In DVPSO, a distribution vector is used in the update of velocity. This vector is adjusted automatically according to the distribution of particles in each dimension. In COPSO, the probabilistic use of a ‘crossing over’ update is introduced to escape from local minima. The landscape adaptive particle swarm optimizer (LAPSO) combines these two schemes with the aim of achieving more robust and efficient search. Empirical performance comparisons between these new modified PSO methods, and also the inertia weight PSO (IFPSO), the constriction factor PSO (CFPSO) and a covariance matrix adaptation evolution strategy (CMAES) are presented on several benchmark problems. All the experimental results show that LAPSO is an efficient method to escape from convergence to local optima and approaches the global optimum rapidly on the problems used.     

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.     

A new iterative mutually coupled hybrid GA–PSO approach for curve fitting in manufacturing

Fitting data points to curves (usually referred to as curve reconstruction) is a major issue in computer-aided design/manufacturing (CAD/CAM). This problem appears recurrently in reverse engineering, where a set of (possibly massive and noisy) data points obtained by 3D laser scanning have to be fitted to a free-form parametric curve (typically a B-spline). Despite the large number of methods available to tackle this issue, the problem is still challenging and elusive. In fact, no satisfactory solution to the general problem has been achieved so far. In this paper we present a novel hybrid evolutionary approach (called IMCH-GAPSO) for B-spline curve reconstruction comprised of two classical bio-inspired techniques: genetic algorithms (GA) and particle swarm optimization (PSO), accounting for data parameterization and knot placement, respectively. In our setting, GA and PSO are mutually coupled in the sense that the output of one system is used as the input of the other and vice versa. This coupling is then repeated iteratively until a termination criterion (such as a prescribed error threshold or a fixed number of iterations) is attained. To evaluate the performance of our approach, it has been applied to several illustrative examples of data points from real-world applications in manufacturing. Our experimental results show that our approach performs very well, being able to reconstruct with very high accuracy extremely complicated shapes, unfeasible for reconstruction with current methods.     

基于成本的MapReduce工作流优化器

对MapReduce栈的不同层进行优化有各自的优缺点。针对MapReduce工作负载的优化问题,提出了相关概念;通过与RoT的对比,介绍了MapReduce工作基于成本的优化及所使用的相关技术,并对MapReduce基于成本的优化进行了评估;基于工作流中的数据流依赖和资源依赖关系,提出了三种工作流优化器,评估了基于成本的工作流优化,并对工作流优化器进行了终端-对-终端的评估;通过实验评估了工作流优化器的优化开销并对这三种工作流优化器的优缺点进行了对比分析。     

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.     

基于模拟退火的粒子群优化算法

粒子群优化算法是一类简单有效的随机全局优化技术。该文把模拟退火思想引入到具有杂交和高斯变异的粒子群优化算法中,给出了一种基于模拟退火的粒子群优化算法。该算法基本保持了粒子群优化算法简单容易实现的特点,但改善了粒子群优化算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。四个基准测试函数的仿真对比结果表明,该算法不仅增强了全局收敛性,而且收敛速度和精度均优于粒子群优化算法。     

A resource-constrained assembly job shop scheduling problem with Lot Streaming technique

To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.     

Parameter estimation of vertical takeoff and landing aircrafts by using a PID controlling particle swarm optimization algorithm

As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles’ local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.     

基于统计的关系数据库查询优化器模型分析与研究

如果没有良好的查询优化器,即使是小型的数据库也会表现出非常明显的性能低下。由于实际优化器的内部结构所涉及的功能和过程异常复杂,通常的商业数据库的查询优化器至少需要50人/年的开发量。主要分析研究关系数据库中的查询优化技术,提出基于统计的、适应于关系数据库的查询优化器设计模型。     

An efficient modified grey wolf optimizer with Lévy flight for optimization tasks

The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO algorithm can reveal an efficient performance compared to other well-established optimizers. However, because of the insufficient diversity of wolves in some cases, a problem of concern is that the GWO can still be prone to stagnation at local optima. In this article, an improved modified GWO algorithm is proposed for solving either global or real-world optimization problems. In order to boost the efficacy of GWO, Lévy flight (LF) and greedy selection strategies are integrated with the modified hunting phases. LF is a class of scale-free walks with randomly-oriented steps according to the Lévy distribution. In order to investigate the effectiveness of the modified Lévy-embedded GWO (LGWO), it was compared with several state-of-the-art optimizers on 29 unconstrained test beds. Furthermore, 30 artificial and 14 real-world problems from CEC2014 and CEC2011 were employed to evaluate the LGWO algorithm. Also, statistical tests were employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of LGWO is significantly better than GWO and other analyzed optimizers.