International Journal of Computational Intelligence Research
Research India Publications
About: International Journal of Computational Intelligence Research is an academic journal published by Research India Publications. The journal publishes majorly in the area(s): Multi-swarm optimization & Metaheuristic. It has an ISSN identifier of 0973-1873. Over the lifetime, 152 publications have been published receiving 3949 citations. The journal is also known as: IJCIR.
Topics: Multi-swarm optimization, Metaheuristic, Particle swarm optimization, Evolutionary algorithm, Artificial neural network
TL;DR: This paper presents a comprehensive review of the vari- ous MOPSOs reported in the specialized literature, and includes a classification of the approaches, and identifies the main features of each proposal.
Abstract: The success of the Particle Swarm Optimiza- tion (PSO) algorithm as a single-objective optimizer (mainly when dealing with continuous search spaces) has motivated re- searchers to extend the use of this bio-inspired technique to other areas. One of them is multi-objective optimization. De- spite the fact that the first proposal of a Multi-Objective Par- ticle Swarm Optimizer (MOPSO) is over six years old, a con- siderable number of other algorithms have been proposed since then. This paper presents a comprehensive review of the vari- ous MOPSOs reported in the specialized literature. As part of this review, we include a classification of the approaches, and we identify the main features of each proposal. In the last part of the paper, we list some of the topics within this field that we consider as promising areas of future research.
TL;DR: A heuristic approach based on particle swarm optimization algorithm is adopted to solving task scheduling problem in grid environment and the results of simulated experiments show that the particle swarm optimized algorithm is able to get the better schedule than genetic algorithm.
Abstract: Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. However, it is a big challenge for efficient scheduling algorithm design and implementation. In this paper, a heuristic approach based on particle swarm optimization algorithm is adopted to solving task scheduling problem in grid environment. Each particle is represented a possible solution, and the position vector is transformed from the continuous variable to the discrete variable. This approach aims to generate an optimal schedule so as to get the minimum completion time while completing the tasks. The results of simulated experiments show that the particle swarm optimization algorithm is able to get the better schedule than genetic algorithm.
TL;DR: This short paper briefly reviews the classical models and the most recent trends for Genetic Fuzzy Systems.
Abstract: Fuzzy Systems have shown their utility for solving a wide range of problems in different application domains. The use of Genetic Algorithms for designing Fuzzy Systems allows us to introduce the learning and adaptation capabilities. This topic has attracted considerable attention in the Computation Intelligence community. This short paper briefly reviews the classical models and the most recent trends for Genetic Fuzzy Systems. We pay special attention to a short discussion on some critical considerations of recent developments and to the suggestion of potential research future directions.
TL;DR: The problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated and an algorithmic framework encompassing both wrapper and filter methodsoffeatureselection is used.
Abstract: In this paper, the problem of unsupervised feature selection and its formulation as a multiobjective optimization problem are investigated. Two existing multiobjective methods from the literature are revisited and used as the basis for an algorithmic framework, encompassing both wrapper and filter methodsoffeatureselection. Anumberofalternativealgorithms implemented within this framework are then evaluated using an extensive data test suite; the main effect investigated is that of thechoiceofaprimaryobjectivefunction(asecondaryobjective function is used only to militate against an inherent cardinality bias affecting all methods of feature subset evaluation). Partic- ular attention is paid in the study to high-dimensional data sets in which the numberof features is much largerthan the number