2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI)

This paper proposes an improved particle swarm optimization (IPSO) algorithm for IIR system identification problem. IPSO adopts three novel steps as follows: The population initialization step is based on golden ratio, which is beneficial for improving the quality of candidate solutions. In velocity updating step, all particles use different inertia weights, which is helpful to preserve the balanc... View full abstract»

In this paper, an improved hybrid encoding firefly algorithm (IFA) is proposed for solving randomized time-varying knapsack problems (RTVKP). The RTVKP is an extension from the generalized time-varying knapsack problems (TVKP) by dynamically changing the profit and weight of items as well as the capacity of knapsack. In IFA, two-tuples composed of real vector and binary vector is used to represent... View full abstract»

This paper compares performance of the artificial bee colony algorithm (ABC) and the real coded genetic algorithm (RCGA) on a suite of 9 standard benchmark problems. The problem suite comprises a diverse set of unimodal, multimodal and rotated multimodal numerical optimization functions and the comparison criteria include (i) solution quality, (ii) convergence speed, (iii) robustness, and (iv) sca... View full abstract»

This study proposes a novel recombination operator, called hybrid crossover operator (HX), which is performed in gene level of chromosome to enhance the optimization performance of multi-objective evolutionary algorithms (MOEAs). The proposed HX operator combines the advantages of simulated binary crossover with local search ability and differential evolution with strong global search capability. ... View full abstract»

It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present multi-objective quantum-inspired cultural algorithms so as to effectively utilize the implicit information embodied in the evolution to promote more efficient search. The dual structure derived from cultural algorithm was adopted. In population space, the rect... View full abstract»

With growing road traffic, it becomes essential to predict the travel time of road travelers well ahead of their travel to avoid traffic mismanagement. Vehicular Adhoc Network (VANET) infrastructure is an ideal solution for such requirements. VANET uses Road Side Units (RSU) that remain as the primary and critical source to predict the traffic in any road. This paper proposes an Optimized RSU base... View full abstract»

The focus of this paper is the memory-less Gravitational Search Algorithm (GSA), which is a unique nature inspired algorithms for continuous optimization, based on the laws of gravity and laws of motion. In order to improve the efficiency, reliability and robustness of GSA, an improved GSA is presented in this paper, which incorporates a simple update mechanism of "best-so-far" particle. The perfo... View full abstract»

Distributing of the multiobjective optimization algorithm into various devices in a parallel fashion is a method for speeding up the computation time of the multiobjective evolutionary algorithms (MOEAs). When the processors are increased in number, the gain from parallelization decreases. Therefore, the aim of the parallelization method is not only to decrease the overall algorithm execution time... View full abstract»

Recently, inspired by migration of monarch butterflies in the Northern American, a new kind of metaheuristic algorithm, called monarch butterfly optimization (MBO), is proposed for solving global optimization problems. It has been experimentally shown that MBO outperforms five state-of-the-art metaheuristic algorithms on most benchmarks. However, the main disadvantage of MBO is that it has poorer ... View full abstract»

This paper presents an approach to comparing and ranking discrete alternatives with respect to multiple, usually conflicting attributes under uncertainty. The uncertainty and imprecision of the human decision making process are adequately modeled with the use of linguistic variables approximated by fuzzy numbers for expressing the decision maker's subjective assessments. The resultant fuzzy assess... View full abstract»

In this paper, a block version of the orthogonal matching pursuit with thresholding algorithm is proposed. Compared with the block version of the orthogonal matching pursuit algorithm, the block orthogonal matching pursuit algorithm works in a less greedy fashion in order to improve support estimating efficiency in each iteration. The lower and upper bounds of the threshold are theoretical derived... View full abstract»

With the trend of aging of society, the number of empty nesters is rising, which has become a social problem that cannot be ignored. In this paper, two empty nester recognition models were presented based on the analysis of calling list and user information table. Based on the normal data, the empty nesters and their children's number can be identified by a recognition function. When the propertie... View full abstract»

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains, for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximiz... View full abstract»

The growth of biomedical and biological research has changed the shape after introduction of microarray technology. Several unsupervised clustering techniques have been introduced in order to explain and interpret the microarray gene expression data sets. A new clustering technique using fuzzy point symmetric concept has been proposed which utilizes particle swarm optimization as the underline opt... View full abstract»

Clustering is an unsupervised classification task of data mining. The high dimensional data sets generally comprise of irrelevant and redundant features also along with the relevant features, which deteriorate the clustering result. Therefore, to improve the clustering result, feature selection is necessary to select a subset of relevant features to improve discrimination ability of the original s... View full abstract»

The increasing popularity of the micro-blogging sites like Twitter, which facilitates users to exchange short messages (aka tweets) is an impetus for data analytics tasks for varied purposes, ranging from business intelligence to nation security. Twitter is being used by a large number of users for events update and sentiment expression. Since tweets are generally unstructured in nature and do not... View full abstract»