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Clustering is a significant data mining task which partitions datasets based on similarities among data. In this study, partitional clustering is considered as an optimization problem and an improved ant-based algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), is applied to automatic grouping of large unlabeled datasets. The proposed algorithm employs Opposition-Based...
Invasive weed optimization (IWO), which is inspired from the invasive behavior of weeds growth in nature, is a population-based intelligence algorithm. However, competitive exclusion may shrink search space and place most seeds in the same local area. Meanwhile, the accurate value of standard deviation is not easy to determine. These two shortcomings may lead to premature convergence and unable to...
It is important for any MOEA (Multi Objective Evolutionary Algorithm) to improve convergence and diversity of solutions of Pareto front, which is obtained at the termination of MOEA. There are many MOEA available in the literature: NSGA-II, SPEA, SPEA2, PESAII and IBEA. This paper aims at improving solutions diversity of Pareto front of a well known multi-objective optimization algorithm, NSGA-II...
Particle swarm optimization (PSO) has been shown as an effective tool for solving single objective optimization problems. However, premature convergence is the major obstacle for PSO. So far, many PSO variants have been proposed to prevent premature convergence. Nonetheless, even though some strategies have been adopted for avoiding premature convergence, PSO variants could not achieve all great performance...
In order to overcome the disadvantages of the K-Means Clustering algorithm, such as the poor global search ability, being sensitive to initial cluster centric, as well as the vulnerable to trap in local optima and the slow convergence velocity in later period of the original Artificial Bee Colony (ABC) algorithm, a Modified ABC algorithm was proposed. Modified Artificial Bee Colony algorithm combined...
This paper provides an overview on a new evolutionary approach based on an intelligent multi-agent architecture to design Beta fuzzy systems (BFSs). The Methodology consists of two processes, a learning process using a clustering technique for the automated design of an initial Beta fuzzy system, and a multi-agent tuning process based on Particle Swarm Optimization algorithm to deal with the optimization...
The shortcomings of traditional serial algorithm on the multi-objective optimization problems are well known for its long computation time and the slow convergence rate, especially when we have complicated computation and large amount of data. To conquer these shortcomings, we propose a parallel multi-objective particle swarm optimization algorithm. Through analyzing the mechanism of multi-objective...
The well-known K-means algorithm has been successfully applied to many practical clustering problems, but it has some drawbacks such as local optimal convergence and sensitivity to initial points. Particle swarm optimization algorithm (PSO) is one of the swarm intelligent algorithms, it is applied in solving global optimization problems. An integration of enhanced PSO and K-means algorithm is becoming...
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