Population Size Reduction in Particle Swarm Optimization Using Product Graphs

Fister, Iztok; Tepeh, Aleksandra; Brest, Janez

doi:10.1007/978-3-319-19824-8_7

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…On the other hand, lack of explicit crossover can lead to local optima. Some methods, for example product graphs that are similar to arithmetic crossover in evolutionary algorithms, are proposed [106] to cope with this problem.…”

Section: Discussionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

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Section: Discussionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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A Population Size Reduction Approach for Nondominated Sorting-Based Optimization Algorithms

Altınöz

Yilmaz

2017

Int. J. Comp. Intel. Appl.

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The solution set of any multi-objective optimization problem can be expressed as an approximation set of Pareto front. The number of solution candidates in this set could be large enough to cover the entire Pareto front as a general belief. However, among the sufficiently close points on the objective space, almost same accurate solutions can obtain. Hence, in this set, it is possible to eliminate some of the solutions without detriment to the overall performance. Therefore, in this research, the authors propose a population size reduction method which systematically reduced the population size based on the distance and angle relations between any consecutive solutions. The results are evaluated based on two-objective benchmark problems and compared with the results of NSGA-II algorithm with respect to three different performance evaluation metrics.

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Population Size Reduction in Particle Swarm Optimization Using Product Graphs

Cited by 2 publications

References 16 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

A Population Size Reduction Approach for Nondominated Sorting-Based Optimization Algorithms

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