Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration
Journal of Network and Computer Applications
Free Pattern Search for global optimization
Applied Soft Computing
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In this paper, a novel population-based optimization algorithm, called Free Search (FS), is studied. First the essential peculiarities of the algorithm is introduced, then the algorithm is improved with the method of changing search neighbor space and preserving excellent members on the basis of sensitivity of the algorithm parameters, thus the improved Free Search Algorithm (iFS) is proposed. Some canonical equations are tested with experiments, and the experimental results shows iFS can speed up the convergence significantly and can avoid the premature convergence effectively. Compared with Free Search and Genetic Algorithm (GA), iFS is found with stable robust behavior on explored results, and can cope with heterogeneous problems.