Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 6 Articles
In this paper, we proposed a new interval-valued approximation of interval-valued fuzzy numbers, which is the best\none with respect to a certain measure of distance between interval-valued fuzzy numbers. Also, a set of criteria for\ninterval-valued approximation operators is suggested....
Deterministic approaches to simultaneously solve different interrelated optimisation problems lead to a\ngeneral class of nonlinear complementarity problem (NCP).Dueto differentiability and convexity requirements\nof the problems, sophisticated algorithms are introduced in literature. This paper develops an\nevolutionary algorithm to solve the NCPs. The proposed approach is a parallel search in which multiple\npopulations representing different agents evolve simultaneously whilst in contact with each other. In this\ncontext, each agent autonomously solves its optimisation programme while sharing its decisions with\nthe neighbouring agents and, hence, it affects their actions. The framework is applied to an environmental\nand an aerospace application where the obtained results are compared with those found in literature.\nThe convergence and scalability of the approach is tested and its search algorithm performance is analysed.\nResults encourage the application of such an evolutionary based algorithm for complementarity\nproblems and future work should investigate its development as well as its performance improvements....
Thermal load in manufacturing processes is of special interest\nas it is closely connected with the surface integrity and life-cycle\nof the finished product. Especially in grinding, heat affected\nzones are created due to excessive heat dissipated within the\nworkpiece during the process. In these zones, defects are created\nthat undermine the quality of the workpiece and as grinding\nis a precision finishing operation, may render it unsuccessful.\nGrinding forces and temperatures are usually studied in\nrelation to the heat affected zones. However, their experimental\nestimation or analytical evaluation may prove laborious and\ncostly. Thus, simulation and modeling techniques are commonly\nemployed for the prediction of these parameters and through\nthem the performance evaluation of the process is performed.\nIn this paper, statistical methods and soft computing techniques,\nnamely regression models completed with analysis of variance,\nand artificial neural networks respectively, are presented for\nthe estimation of grinding forces and temperature. A brief\ndescription of the models and a comparative study is performed,\nbased on experimental results. Both modeling tools prove to\nbe quite successful, predicting with high accuracy forces and\ntemperatures....
This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the\nmodel optimizes simultaneously the parameters of phase space reconstruction (...
Document clustering is widely used in Information Retrieval\nhowever, existing clustering techniques suffer from local optima problem in\ndetermining the k number of clusters. Various efforts have been put to\naddress such drawback and this includes the utilization of swarm-based\nalgorithms such as particle swarm optimization and Ant Colony\nOptimization. This study explores the adaptation of another swarm\nalgorithm which is the Firefly Algorithm (FA) in text clustering. We\npresent two variants of FA; Weight- based Firefly Algorithm (WFA) and\nWeight-based Firefly Algorithm II (WFAII). The difference between the\ntwo algorithms is that the W FAII, includes a more restricted condition in\ndetermining members of a cluster. The proposed FA methods are later\nevaluated using the 20 News groups dataset. Experimental results on the\nquality of clustering between the two FA variants are presented and are\nlater compared against the one produced by particle swarm optimization,\nK-means and the hybrid of FA and -K-means. The obtained results\ndemonstrated that the W FAII outperformed the WFA, PSO, K-means and\nFA-K means. This result indicates that a better clustering can be obtained\nonce the exploitation of a search solution is improved....
Software development effort estimation is considered a fundamental task for software development\nlife cycle as well as for managing project cost, time and quality. Therefore, accurate estimation\nis a substantial factor in projects success and reducing the risks. In recent years, software effort\nestimation has received a considerable amount of attention from researchers and became a\nchallenge for software industry. In the last two decades, many researchers and practitioners proposed\nstatistical and machine learning-based models for software effort estimation. In this work,\nFirefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters\nof three COCOMO-based models. These models include the basic COCOMO model and other\ntwo models proposed in the literature as extensions of the basic COCOMO model. The developed\nestimation models are evaluated using different evaluation metrics. Experimental results show\nhigh accuracy and significant error minimization of Firefly Algorithm over other metaheuristic\noptimization algorithms including Genetic Algorithms and Particle Swarm Optimization....
Loading....