Quantum-Inspired Evolution Algorithm: Experimental Analysis

Abstract

Quantum computing mimics behaviour of atoms in processing information. Unfortunately due to restrictive rules of processing imposed by quantum behaviour only few successful algorithms have been developed in quantum computing. Quantum inspired algorithm is a concept, which employs certain elements of quantum computing to use in a wider class of search and optimisation problems. The main parts of a quantum‐inspired algorithm are the qubits (quantum equivalent of bits) and the gates. Qubits hold the information in a superposition of all the states, while the quantum gates evolve the qubit to achieve the desired objective, which is, in optimization the maximum or the minimum. The paper addresses the ability of the Quantum‐Inspired Evolution Algorithm (QIEA) to solve practical engineering problems. QIEA, which is developed by authors, is based on their previous work and it is improved to test a series of unitary gates. A set of experiments were carried out to investigate the performance of QIEA as for speed, accuracy, robustness, simplicity, generality, and innovation. To assess effectiveness of a new algorithm, there are a set of guidelines proposed by [1]. Based on these guidelines, the paper selected three test functions to carry out a benchmark study. The paper also presents a comparative study between QIEA and classical Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) techniques in order to assess the proposed QIEA.