An Adaptive Quantum-inspired Differential Evolution Algorithm for 0-1 Knapsack Problem

TL;DR

This paper introduces AQDE, an innovative algorithm combining adaptive differential evolution with quantum-inspired techniques, significantly improving solutions for the 0-1 knapsack problem over existing methods.

Contribution

It presents the first adaptive quantum-inspired differential evolution algorithm tailored for binary combinatorial optimization problems.

Findings

AQDE outperforms QEA and discrete DE on standard 0-1 knapsack tests.

AQDE shows significant improvements across all test cases.

The adaptive quantum-inspired approach enhances optimization performance.

Abstract

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. However, the design of its operators makes it unsuitable for many real-life constrained combinatorial optimization problems which operate on binary space. On the other hand, the quantum inspired evolutionary algorithm (QEA) is very well suitable for handling such problems by applying several quantum computing techniques such as Q-bit representation and rotation gate operator, etc. This paper extends the concept of differential operators with adaptive parameter control to the quantum paradigm and proposes the adaptive quantum-inspired differential evolution algorithm (AQDE). The performance of AQDE is found to be significantly superior as compared to QEA and a discrete version of DE on the standard 0-1 knapsack problem for all the considered test cases.