Modified genetic programming combining with particle swarm optimization and performance criterion in solar cell fabrication

This study describes the design and development of the novel model for the process optimization of solar cell fabrication. The model performance can affect the result of the physical experiment in the solar cell fabrication because the high accuracy model can provide the closer result to the output efficiency of the physical experiment. In this study, genetic programming (GP) based modeling technique was developed for the process simulation. GP is a global modeling technique, so it is suitable for process data modeling. This study describes the modified GP algorithm to solve the constant terminal problem. In the traditional GP, the constant term can be randomly selected within the fixed range when the structure is changed. Therefore, the variation ratio of the constant is too low to fit the model well. In this study, the novel GP is proposed. The method includes particle swarm optimization (PSO) to optimize the constant term in the terminals. PSO is a strong searching algorithm without a high computation cost. Actually, through the simulation results, the modeling performance and speed can be improved by the proposed GP. Because by the proposed modeling method, the structure and parameters of the model can be optimized simultaneously, the proposed method can be used as the new global modeling approach.     

Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.     

A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates.     

Gene selection using hybrid particle swarm optimization and genetic algorithm

Selecting high discriminative genes from gene expression data has become an important research. Not only can this improve the performance of cancer classification, but it can also cut down the cost of medical diagnoses when a large number of noisy, redundant genes are filtered. In this paper, a hybrid Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) method is used for gene selection, and Support Vector Machine (SVM) is adopted as the classifier. The proposed approach is tested on three benchmark gene expression datasets: Leukemia, Colon and breast cancer data. Experimental results show that the proposed method can reduce the dimensionality of the dataset, and confirm the most informative gene subset and improve classification accuracy.     

Improving solution characteristics of particle swarm optimization using digital pheromones

In this paper, a new approach to particle swarm optimization (PSO) using digital pheromones to coordinate swarms within an n-dimensional design space is presented. In a basic PSO, an initial randomly generated population swarm propagates toward the global optimum over a series of iterations. The direction of the swarm movement in the design space is based on an individual particle’s best position in its history trail (pBest), and the best particle in the entire swarm (gBest). This information is used to generate a velocity vector indicating a search direction toward a promising location in the design space. The premise of the research presented in this paper is based on the fact that the search direction for each swarm member is dictated by only two candidates—pBest and gBest, which are not efficient to locate the global optimum, particularly in multi-modal optimization problems. In addition, poor move sets specified by pBest in the initial stages of optimization can trap the swarm in a local minimum or cause slow convergence. This paper presents the use of digital pheromones for aiding communication within the swarm to improve the search efficiency and reliability, resulting in improved solution quality, accuracy, and efficiency. With empirical proximity analysis, the pheromone strength in a region of the design space is determined. The swarm then reacts accordingly based on the probability that this region may contain an optimum. The additional information from pheromones causes the particles within the swarm to explore the design space thoroughly and locate the solution more efficiently and accurately than a basic PSO. This paper presents the development of this method and results from several multi-modal test cases.     

Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The papers new contributions to multidisciplinary optimization are the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations for the utilization of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization, as well as the numerical noise and discrete variables present in the current example problem.     

Automated clustering of trajectory data using a particle swarm optimization

Clustering trajectory data discovers and visualizes available structure in movement patterns of mobile objects and has numerous potential applications in traffic control, urban planning, astronomy, and animal science. In this paper, an automated technique for clustering trajectory data using a Particle Swarm Optimization (PSO) approach has been proposed, and Dynamic Time Warping (DTW) distance as one of the most commonly-used distance measures for trajectory data is considered. The proposed technique is able to find (near) optimal number of clusters as well as (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method (in terms of a certain performance index), a Discrete Cosine Transform (DCT) representation of cluster centers is considered. The proposed method is able to admit various cluster validity indexes as objective function for optimization. Experimental results over both synthetic and real-world datasets indicate the superiority of the proposed technique to fuzzy C-means, fuzzy K-medoids, and two evolutionary-based clustering techniques proposed in the literature.     

动态规划-粒子群算法在水库优化调度中的应用

水库优化调度是一个典型的具有多约束条件的、动态的、非线性的优化问题。针对这些问题,利用动态规划-粒子群(DP-PSO)算法加以求解。利用动态规划中的多阶段最优策略原理,将水库优化调度问题转化为多阶段决策子问题,各个子问题采用粒子群算法优化求解。数值实验表明,在计算时段较多时,DP-PSO算法计算的可靠性明显优于一般的动态规划(DP)算法,在计算时间上,DP-PSO算法用时较动态规划-遗传算法(DP-GA)少。     

Shadow detecting using particle swarm optimization and the Kolmogorov test

An algorithm combining both gray level information and geometric features is introduced to detect cast shadows in gray level images. A simply connected candidate shadow region and a corresponding region are segmented by setting gray level thresholds, and neighbor-matching regions are constructed with a mathematical morphological algorithm. A shadow-non-shadow region pair is obtained from the result of Kolmogorov test for statistical features of both candidate neighbor-matching regions. Shadow regions are obtained by selecting the region with relatively lower average gray level from the matched region pair. The particle swarm optimization (PSO) algorithm is used to facilitate the feature extraction during the matching process. Experimental results showed the effectiveness of the proposed algorithm for cast shadow detecting in a single gray level image.     

一种遗传算法与粒子群优化的多子群分层混合算法

针对遗传算法全局搜索能力强和粒子群优化收敛速度快的特点, 本文从种群个体组织结构上着手, 进行优势互补, 提出了一种遗传算法和粒子群优化的多子群分层混合算法(multi-subgroup hierarchical hybrid of genetic algorithm and particle swarm optimization, HGA–PSO). 算法采用分层结构, 底层由一系列的遗传算法子群组成, 贡献算法的全局搜索能力; 上层是由每个子群的最优个体组成的精英群, 采用钳制了初始速度的粒子群算法进行精确局部搜索. 文中分析论证了HGA–PSO算法具有全局收敛性, 并采用7个典型高维Benchmark函数进行测试, 实验结果显示该算法的优化性能显著优于其他测试算法.     

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors’ ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.     

混合粒子群算法在微电网经济优化运行的应用

微电网经济优化运行是一个连续的多约束优化问题,其不仅需要对同一时刻的不同微源作出优化,也要协调微源在不同时刻的出力来满足电力供给和负荷需求之间的约束.针对微电网经济优化运行问题,提出一种混合粒子群算法.该算法在随机权重平衡粒子群算法的基础上,引入了免疫机制,使初始粒子的位置较为均匀地分布在坐标平面内.而对于粒子的速度与方向,提出一个非线性权重以提升算法的寻优能力,并加入了次梯度寻优,加快了算法的收敛速度.通过该算法对微电网中可控微源的输出功率做出动态部署,引入微电网系统的市场机制可以有效调配各微源的输出功率,从而提升微电网运行的经济效益.通过对微电网孤岛和并网运行方式进行实例仿真,验证了该方法对微电网经济优化问题具有良好的经济优化作用.     

微粒群算法中粒子运动稳定性分析

在研究微粒群算法是否收敛时,粒子运动稳定是微粒群算法收敛的前提条件,在分析粒子运动稳定性时,大多数文献假定微粒群只有单个粒子,最优粒子位置和局部最优粒子位置固定不动,并且忽略粒子运动的随机性,这些假定忽视了粒子算法中粒子运动的本质.首先从评估函数出发,考虑到粒子间的交换性,给出了吸引位置存在的证明,然后利用随机过程理论对粒子的运动进行分析,证明了最优粒子的位置序列是不断靠近吸引位置,最后考虑粒子运动的随机性,利用时变差分系统理论,构造李亚普诺夫能量函数,得到了微粒群中任意粒子运动稳定的条件.     

基于遗传算子的粒子群优化算法的比较分析

为了深入分析探讨改进的粒子群优化算法的性能,针对典型的函数优化问题,设计了3种方案：(1)采用线性递减惯性权重的PSO;(2)基于遗传算子的PSO;(3)在方案(2)基础上,加入收缩因子χ。在MATLAB 7.0中对常用的测试函数进行优化仿真,发现当融合遗传算子和收缩因子时,算法性能最优。     

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.     

Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.     

Multidisciplinary design of a small satellite launch vehicle using particle swarm optimization

In the present paper, particle swarm optimization, a relatively new population based optimization technique, is applied to optimize the multidisciplinary design of a solid propellant launch vehicle. Propulsion, structure, aerodynamic (geometry) and three-degree of freedom trajectory simulation disciplines are used in an appropriate combination and minimum launch weight is considered as an objective function. In order to reduce the high computational cost and improve the performance of particle swarm optimization, an enhancement technique called fitness inheritance is proposed. Firstly, the conducted experiments over a set of benchmark functions demonstrate that the proposed method can preserve the quality of solutions while decreasing the computational cost considerably. Then, a comparison of the proposed algorithm against the original version of particle swarm optimization, sequential quadratic programming, and method of centers carried out over multidisciplinary design optimization of the design problem. The obtained results show a very good performance of the enhancement technique to find the global optimum with considerable decrease in number of function evaluations.     

Solving the Urban Transit Routing Problem using a particle swarm optimization based algorithm

The Urban Transit Routing Problem (UTRP) comprises an NP-hard problem that deals with the construction of route networks for public transit networks. It is a highly complex and multiply constrained problem, in which the assessment of candidate route networks can be both time consuming and challenging. Except for that, a multitude of potential solutions are usually rejected due to infeasibility. Because of this difficulty, soft computing algorithms can be very effective for its efficient solution. The success of these methods, however, depends mainly on the quality of the representation of candidate solutions, on the efficiency of the initialization procedure and on the suitability of the modification operators used.An optimization algorithm, based on particle swarm optimization, is designed and presented in the current contribution, aiming at the efficient solution of UTRP. Apart from the development of the optimization algorithm, emphasis is also given on appropriate representation of candidate solutions, the route networks in other words, and the respective evaluation procedure. The latter procedure considers not only the quality of service offered to each passenger, but also the costs of the operator. Results are compared on the basis of Mandl's benchmark problem of a Swiss bus network, which is probably the only widely investigated and accepted benchmark problem in the relevant literature. Comparison of the obtained results with other results published in the literature shows that the performance of the proposed soft computing algorithm is quite competitive compared to existing techniques.     

Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach

The Journal of Supercomputing - Network functions virtualization (NFV) is a new concept that has received the attention of both researchers and network providers. NFV decouples network functions...