Training Multi-layer Perceptron Using Hybridization of Chaotic Gravitational Search Algorithm and Particle Swarm Optimization

Sajad Ahmad Rather, P. Shanthi Bala and Pillai Lekshmi Ashokan
Additional contact information
Sajad Ahmad Rather: Pondicherry University
P. Shanthi Bala: Pondicherry University
Pillai Lekshmi Ashokan: Pondicherry University

Chapter Chapter 13 in Applying Particle Swarm Optimization, 2021, pp 233-262 from Springer

Abstract: Abstract A novel amalgamation strategy, namely, chaotic gravitational search algorithm (CGSA) and particle swarm optimization (PSO), has been employed for training multi-layer perceptron (MLP) neural network. It is called CGSAPSO. In CGSAPSO, exploration is carried out by CGSA, and exploitation is performed using PSO. The sigmoid activation function is utilized for training MLP. Besides, a matrix encoding strategy has been used for providing a synergy between neural biases, weights, and CGSAPSO searcher agents. To validate the effectiveness of the hybrid framework, CGSAPSO is applied to three different classification datasets, namely, XOR, Iris, and Balloon. The investigation of results is carried out through various performance metrics like average, standard deviation, median, convergence speed, execution time, and classification rate analysis. Besides, a pair-wise non-parametric signed Wilcoxon rank-sum test has also been conducted for statistical verification of simulation results. In addition, the numerical outcomes of CGSAPSO are also compared with standard GSA, PSO, and hybrid PSOGSA. The experimental results indicate that CGSAPSO provides better results in the form of recognition accuracy and global optima as compared to competing algorithms.

Keywords: Gravitational Search Algorithm; Chaotic maps; Hybridization; Optimization; Swarm intelligence; Multi-layer perceptron (MLP); Particle swarm optimization (PSO); Exploration; Exploitation (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-70281-6_13

Ordering information: This item can be ordered from
http://www.springer.com/9783030702816

DOI: 10.1007/978-3-030-70281-6_13

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().