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Authors
Affiliations
1 Department of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203, BD
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 2, No 4 (2010), Pagination: 172-187
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.
Keywords
Hybrid Algorithm, Evolutionary Algorithm, Particle Swarm Optimization, Quantum Evolutionary Algorithm, Arithmetic Crossover.
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