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Metaheuristic Search Algorithms in Solving the n-Similarity Problem

Mirhosseini M., Nezamabadi-pour H.

Fundamenta Informaticae

2017

Vol. 152, nr 2

145--166

The - similarity problem, finding a group of objects which have the most similarity to each other, has become an important issue in information retrieval and data mining. The theory of this concept is mathematically proven, but it practically has high time complexity. Binary Genetic Algorithm (BGA) has been applied to improve solutions quality of this problem, but a more efficient algorithm is required. Therefore, we aim to study and compare the performance of four metaheuristic algorithms called Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Imperialist Competitive Algorithm (ICA) and Fuzzy Imperialist Competitive Algorithm (FICA) to tackle this problem. The experiments are conducted on two applications; the former is on four UCI datasets as a general application and the latter is on the text resemblance application to detect multiple similar text documents from Reuters datasets as a case study. The results of experiments give a ranking of the algorithms in solving the -similarity problem in both applications based on the exploration and exploitation abilities, that the FICA achieves the first rank in both applications as well as based on the both criteria.

A Clustering Based Archive Multi Objective Gravitational Search Algorithm

Abbasian M. A., Nezamabadi-pour H., Amoozegar M.

Fundamenta Informaticae

2015

Vol. 138, nr 4

387--409

Gravitational search algorithm(GSA) is a recent createdmetaheuristic optimization algorithm with good results in function optimization as well as real world optimization problems. Many real world problems involve multiple (often conflicting) objectives, which should be optimized simultaneously. Therefore, the aim of this paper is to propose a multi-objective version of GSA, namely clustering based archive multi-objective GSA (CA-MOGSA). Proposed method is created based on the Pareto principles. Selected non-dominated solutions are stored in an external archive. To control the size of archive, the solutions with less crowding distance are removed. These strategies guarantee the elitism and diversity as two important features of multi-objective algorithms. The archive is clustered and a cluster is randomly selected for each agent to apply the gravitational force to attract it. The selection of the proper cluster is based on the distance between clusters representatives and population member (the agent). Therefore, suitable trade-off between exploration and exploitation is provided. The experimental results on eight standard benchmark functions reveal that CA-MOGSA is a well-organized multi-objective version of GSA. It is comparable with the state-ofthe- art algorithms including non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm (SPEA2) and better than multi-objective GSA (MOGSA), time-variant particle swarm optimization (TV-PSO), and non-dominated sorting GSA (NSGSA).