Benchmarking of GPU-optimized Quantum-Inspired Evolutionary Optimization Algorithm using Functional Analysis

TL;DR

This paper compares GPU-optimized quantum-inspired evolutionary optimization (QIEO) with genetic algorithms (GA) on complex benchmark functions, demonstrating QIEO's superior efficiency, consistency, and potential as an alternative for challenging optimization problems.

Contribution

It provides a comprehensive benchmarking of GPU-accelerated QIEO against classical GAs on complex functions, highlighting QIEO's improved performance and reliability.

Findings

QIEO requires fewer function evaluations than GA.

QIEO reduces optimization time by approximately three to four times.

QIEO shows more consistent convergence across trials.

Abstract

This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classical metaheuristic techniques, the genetic algorithm (GA). The study assesses the performance of both algorithms on highly non-linear, non-convex, and non-separable function optimization problems, viz., Ackley, Rosenbrock, and Rastrigin, that are representative of the complex real-world optimization problems. The performance of these algorithms is checked by varying the population sizes by keeping all other parameters constant and comparing the fitness value it reached along with the number of function evaluations they required for convergence. The results demonstrate that QIEO performs better for these functions than GA, by achieving the target fitness with fewer function evaluations and significantly reducing the total optimization time approximately three times for the Ackley function and four times for the Rosenbrock and Rastrigin functions. Furthermore, QIEO exhibits greater consistency across trials, with a steady convergence rate that leads to a more uniform number of function evaluations, highlighting its reliability in solving challenging optimization problems. The findings indicate that QIEO is a promising alternative to GA for these kind of functions.