Advances in Swarm Intelligence: 5th International by Ying Tan, Yuhui Shi, Carlos A Coello Coello

By Ying Tan, Yuhui Shi, Carlos A Coello Coello

This publication and its better half quantity, LNCS vol. 8794 and 8795 represent the complaints of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers awarded have been conscientiously reviewed and chosen from 198 submissions. The papers are prepared in 18 cohesive sections, three particular periods and one aggressive consultation overlaying all significant issues of swarm intelligence examine and improvement akin to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; man made bee colony algorithms; man made immune process; evolutionary algorithms; neural networks and fuzzy equipment; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; category tools; GPU-based tools; scheduling and direction making plans; instant sensor networks; strength procedure optimization; swarm intelligence in picture and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world application.

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Keywords: feature selection, rough set, fish swarm algorithm, ant colony optimization, chaotic binary particle swarm optimization. 1 Introduction Feature selection is the process of choosing a good subset of relevant features and eliminating redundant ones from an original feature set, which can be perceived as a principal pre-processing tool for solving the classification problem [1]. The main objective of feature selection is to find a minimal feature subset from a set of features with high performance in representing the original features [2].

In PSO, a particle represents a candidate solution to the problem, which has its own velocity and position in a given search space. PSO starts with the stochastic initialization of a population of particles which move in the search space to find the optimal solution by updating the position of each particle by using its own experience and its companion’s experience [9]. Assume a swarm includes N particles which move around in a D-dimensional search space. The velocity of the ith particle in different space can be represented by vi = (vi1 , vi 2 , , viD ) , and the position for the ith particle in different space can be noted as xi = ( xi1 , xi 2 , , xiD ) .

1. 1 Datasets and Parameters Setting To evaluate the usefulness of the proposed algorithms, we carry out experiments on six datasets of the UCI machine learning repository. In Dermatology (Der) dataset, some samples are missed in age feature, so it is removed. In the experiments, the five algorithms require additional parameter settings for their operations. 0. 5, the number of ants is half the number of features and the maximum iteration equals 50. 0 , population size P = 20, maximum iteration T = 500, velocity Vmax = 4, Vmin = −4 .

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