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This paper proposes a generalized Hybrid Real-coded Quantum EvolutionaryAlgorithm (HRCQEA) for optimizing complex functions as well as combinatorialoptimization. The main idea of HRCQEA is to devise a new technique for mutationand crossover operators. Using the evolutionary equation of PSO aSingle-Multiple gene Mutation (SMM) is designed and the concept of ArithmeticCrossover (AC) is used in the new Crossover operator. In HRCQEA, each triploidchromosome represents a particle and the position of the particle is updatedusing SMM and Quantum Rotation Gate (QRG), which can make the balance betweenexploration and exploitation. Crossover is employed to expand the search space,Hill Climbing Selection (HCS) and elitism help to accelerate the convergencespeed. Simulation results on Knapsack Problem and five benchmark complexfunctions with high dimension show that HRCQEA performs better in terms ofability to discover the global optimum and convergence speed.

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

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