A Generalized Hybrid Real-Coded Quantum Evolutionary Algorithm Based on Particle Swarm Theory with Arithmetic Crossover

TL;DR

This paper introduces a novel hybrid quantum evolutionary algorithm combining particle swarm theory with arithmetic crossover, enhancing global optimization and convergence speed for complex functions and combinatorial problems.

Contribution

It develops a generalized hybrid real-coded quantum evolutionary algorithm integrating new mutation and crossover techniques based on PSO and quantum principles.

Findings

Outperforms existing algorithms in global optimum discovery

Achieves faster convergence on high-dimensional problems

Effective on knapsack and benchmark functions

Abstract

This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial optimization. The main idea of HRCQEA is to devise a new technique for mutation and crossover operators. Using the evolutionary equation of PSO a Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic Crossover (AC) is used in the new Crossover operator. In HRCQEA, each triploid chromosome represents a particle and the position of the particle is updated using SMM and Quantum Rotation Gate (QRG), which can make the balance between exploration and exploitation. Crossover is employed to expand the search space, Hill Climbing Selection (HCS) and elitism help to accelerate the convergence speed. Simulation results on Knapsack Problem and five benchmark complex functions with high dimension show that HRCQEA performs better in terms of ability to discover the global optimum and convergence speed.