Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

Author:

Abdel-Basset Mohamed1ORCID,Mohamed Reda1,Hezam Ibrahim M.2ORCID,Sallam Karam M.3ORCID

Affiliation:

1. Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah 44519, Egypt

2. Department of Statistics & Operations Research, College of Sciences, King Saud University, Riyadh, Saudi Arabia

3. Faculty of Science and Technology, School of IT and Systems, University of Canberra, Canberra, ACT 2601, Australia

Abstract

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.

Funder

King Saud University

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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