A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters

In this paper a fuzzy point symmetry based genetic clustering technique (Fuzzy-VGAPS) is proposed which can automatically determine the number of clusters present in a data set as well as a good fuzzy partitioning of the data. The clusters can be of any size, shape or convexity as long as they possess the property of symmetry. Here the membership values of points to different clusters are computed using the newly proposed point symmetry based distance. A variable number of cluster centers are encoded in the chromosomes. A new fuzzy symmetry based cluster validity index, FSym-index is first proposed here and thereafter it is utilized to measure the fitness of the chromosomes. The proposed index can detect non-convex, as well as convex-non-hyperspherical partitioning with variable number of clusters. It is mathematically justified via its relationship to a well-defined hard cluster validity function: the Dunn’s index, for which the condition of uniqueness has already been established. The results of the Fuzzy-VGAPS are compared with those obtained by seven other algorithms including both fuzzy and crisp methods on four artificial and four real-life data sets. Some real-life applications of Fuzzy-VGAPS to automatically cluster the gene expression data as well as segmenting the magnetic resonance brain image with multiple sclerosis lesions are also demonstrated.     

Simultaneous feature selection and symmetry based clustering using multiobjective framework

In this paper a new framework based on multiobjective optimization (MOO), namely FeaClusMOO, is proposed which is capable of identifying the correct partitioning as well as the most relevant set of features from a data set. A newly developed multiobjective simulated annealing based optimization technique namely archived multiobjective simulated annealing (AMOSA) is used as the background strategy for optimization. Here features and cluster centers are encoded in the form of a string. As the objective functions, two internal cluster validity indices measuring the goodness of the obtained partitioning using Euclidean distance and point symmetry based distance, respectively, and a count on the number of features are utilized. These three objectives are optimized simultaneously using AMOSA in order to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning. Points are allocated to different clusters using a point symmetry based distance. Mutation changes the feature combination as well as the set of cluster centers. Since AMOSA, like any other MOO technique, provides a set of solutions on the final Pareto front, a technique based on the concept of semi-supervised classification is developed to select a solution from the given set. The effectiveness of the proposed FeaClustMOO in comparison with other clustering techniques like its Euclidean distance based version where Euclidean distance is used for cluster assignment, a genetic algorithm based automatic clustering technique (VGAPS-clustering) using point symmetry based distance with all the features, K-means clustering technique with all features is shown for seven higher dimensional data sets obtained from real-life.     

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.     

Genetic clustering for automatic evolution of clusters and application to image classification

In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies-Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.     

A robust design of simulated annealing approach for mixed-model sequencing

The effectiveness of the solution method based on simulated annealing (SA) mainly depends on how to determine the SA-related parameters. A scheme as well as parameter values for defining an annealing schedule should be appropriately determined, since various schemes and their corresponding parameter values have a significant impact on the performance of SA algorithms. In this paper, based on robust design we propose a new annealing parameter design method for the mixed-model sequencing problem which is known to be NP-hard. To show the effectiveness of the proposed method, extensive computation experiments are conducted. It was found that the robust designed method outperforms the SA algorithm by McMullen and Frazier [McMullen, P.R., & Frazier, G.V. (2000). A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line. IIE Transactions, 32, 679–686].     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

A multi-objective mixed-model assembly line sequencing problem in order to minimize total costs in a Make-To-Order environment,considering order priority

Nowadays, mixed-model assembly line is used increasingly as a result of customers’ demand diversification. An important problem in this field is determining the sequence of products for entering the line. Before determining the best sequence of products, a new procedure is introduced to choose important orders for entering the shop floor. Thus the orders are sorted using an analytical hierarchy process (AHP) approach based on three criteria: critical ratio of each order (CR_o), Significance degree of customer and innovation in a product, while the last one is presented for the first time. In this research, six objective functions are presented: minimizing total utility work cost, total setup cost and total production rate variation cost are the objectives which were presented previously, another objective is minimizing total idle cost, meanwhile two other new objectives regarding minimizing total operator error cost and total tardiness cost are presented for the first time. The total tardiness cost tries to choose a sequence of products that minimizes the tardiness cost for customers with high priority. First, to check the feasibility of the model, GAMS software is used. In this case, GAMS software could not search all of the solution space, so it is tried in two stages and because this problem is NP-hard, particle swarm optimization (PSO) and simulated annealing (SA) algorithms are used. For small sized problems, to compare exact method with proposed algorithms, the problem must be solved using meta-heuristic algorithms in two stages as GAMS software, whereas for large sized problems, the problem can be solved in two ways (one stage and two stages) by using proposed algorithms; the computational results and pairwise comparisons (based on sign test) show GAMS is a proper software to solve small sized problems, whereas for a large sized problem the objective function is better when solved in one stage than two stages; therefore it is proposed to solve the problem in one stage for large sized problems. Also PSO algorithm is better than SA algorithm based on objective function and pairwise comparisons.     

A novel clock generation algorithm for system-on-chip based on least common multiple

Due to increase in the number of Intellectual Property (IP) cores, clock generation in current day System-on-Chips (SoCs) is facing a crisis. The conventional method of using a dedicated Phase Locked Loop (PLL) to generate the clock for each IP core is becoming inefficient in terms of power and cost. We propose an algorithm based on Least Common Multiple (LCM) to minimize the number of PLLs required to generate the clocks for the IP cores in a SoC. This is done by finding an Optimum Operating Frequency (OOF) for each IP core within 10% below the maximum operating frequency of the core. The OOF is chosen such that the LCM of the OOF of all the IP cores is minimized. Simulated annealing is used to find the LCM. This LCM is the crucial high frequency from which maximum number of clocks can be derived by clock dividers.     

Development of decoupling scheme for high order MIMO process based on PSO technique

Multiple-input multiple-output (MIMO) with N Input/ N Output processes are characterized by significant interactions between their inputs and outputs. The control of MIMO processes is usually implemented using sets of single-input single-output (SISO) loop controllers, which requires proper input–output pairing and development of decoupling compensator unit. In this paper, a generalized decoupling technique is proposed. The proposed technique uses relative gain array (RGA) to select proper pairing and particle swarm optimization (PSO) technique to estimate the optimal elements’ values of steady state decoupling compensation matrix constituting the decoupling compensator unit. The proposed technique is applied on 4 Input/ 4 Output two coupled distillation columns process, it proves remarkable success in minimizing the interaction between every input and all outputs except that output has been proper paired with.     

S-IDE: A tool framework for optimizing deployment architecture of High Level Architecture based simulation systems

One of the important problems in High Level Architecture (HLA) based distributed simulation systems is the allocation of the different simulation modules to the available physical resources. Usually, the deployment of the simulation modules to the physical resources can be done in many different ways, and each deployment alternative will have a different impact on the performance. Although different algorithmic solutions have been provided to optimize the allocation with respect to the performance, the problem has not been explicitly tackled from an architecture design perspective. Moreover, for optimizing the deployment of the simulation system, tool support is largely missing. In this paper we propose a method for automatically deriving deployment alternatives for HLA based distributed simulation systems. The method extends the IEEE Recommended Practice for High Level Architecture Federation Development and Execution Process by providing an approach for optimizing the allocation at the design level. The method is realized by the tool framework, S-IDE (Simulation-IDE) that we have developed to provide an integrated development environment for deriving a feasible deployment alternative based on the simulation system and the available physical resources at the design phase. The method and the tool support have been validated using a case study for the development of a traffic simulation system.