Hybrid Quantum Genetic Algorithm for the 0-1 Knapsack Problem in the IBM Qiskit Simulator

Ballinas, Enrique; Montiel, Oscar

doi:10.13053/cys-26-2-4253

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To create the binary variant of MPA (BMPA), a variety of V-Shaped and S-Shaped transfer functions for transferring continuous values to binary are examined. Tere are several other recently published algorithms for KP01, some of which are the binary slime-mould algorithm [36], binary salp swarm algorithm [63], binary light spectrum optimizer [38], hybrid rice optimization algorithm [64], hybrid harmony search algorithm [65], hybrid quantum genetic algorithm [66], binary gainingsharing knowledge-based optimization algorithm [67], and improved binary pigeon inspired optimization algorithm [68].…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract