Abstract
This paper proposes a novel hybrid algorithm based on quantum-behaved particle swarm optimization (QPSO) algorithm and Nelder-Mead (NM) simplex search method for continuous optimization problems, abbreviated as QPSO-NM. This hybrid algorithm is very easy to be implemented since it does not require continuity and differentiability of objective functions, and it also combines powerful global search ability of QPSO with precise local search of NM simplex method. In a suite of the first 10 test functions taken from CEC2005, QPSO-NM algorithm is compared with other four popular competitors and six special algorithms that are dedicated to solve CEC2005 test function suite. It is showed by the computational results that QPSO-NM outperforms other algorithms in terms of both convergence rate and solution accuracy. The proposed algorithm is extremely effective and efficient at locating optimal solutions for continues optimization.
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