`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Journal of Global Optimization
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
Journal of Global Optimization
A heuristic iterated-subspace minimization method with pattern search for unconstrained optimization
Computers & Mathematics with Applications
Free Search-a comparative analysis
Information Sciences: an International Journal
Research and Improvement of Free Search Algorithm
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 01
Journal of Computational Physics
Integrated Learning Particle Swarm Optimizer for global optimization
Applied Soft Computing
A clustering-based differential evolution for global optimization
Applied Soft Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Cellular particle swarm optimization
Information Sciences: an International Journal
Learning-enhanced differential evolution for numerical optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Computation on General Purpose Graphics Processing Units
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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An efficient algorithm named Pattern search (PS) has been used widely in various scientific and engineering fields. However, even though the global convergence of PS has been proved, it does not perform well on more complex and higher dimension problems nowadays. In order to improve the efficiency of PS and obtain a more powerful algorithm for global optimization, a new algorithm named Free Pattern Search (FPS) based on PS and Free Search (FS) is proposed in this paper. FPS inherits the global search from FS and the local search from PS. Two operators have been designed for accelerating the convergence speed and keeping the diversity of population. The acceleration operator inspired by FS uses a self-regular management to classify the population into two groups and accelerates all individuals in the first group, while the throw operator is designed to avoid the reduplicative search of population and keep the diversity. In order to verify the performance of FPS, two famous benchmark instances are conducted for the comparisons between FPS with Particle Swarm Optimization (PSO) variants and Differential Evolution (DE) variants. The results show that FPS obtains better solutions and achieves the higher convergence speed than other algorithms.