A Linear Algorithm for Generating Random Numbers with a Given Distribution
IEEE Transactions on Software Engineering
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Tensor calculations on computer: appendix
Communications of the ACM
Nested Partitions Method for Global Optimization
Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Discrete Optimization via Simulation Using COMPASS
Operations Research
Speeding up COMPASS for high-dimensional discrete optimization via simulation
Operations Research Letters
Multiobjective learning in the random neural network
International Journal of Advanced Intelligence Paradigms
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Search algorithms are often used for optimization problems where its mathematical formulation is difficult to be analyzed, e.g., simulation optimization. In literature, search algorithms are either driven by gradient or based on random sampling within specified neighborhood, but both methods have limitation as gradient search can be easily trapped at a local optimum and random sampling loses efficiency by not utilizing local information such as gradient direction that might be available. A combination of the two is believed to overcome both disadvantages. However, the main difficulty is how to incorporate and control randomness in a direction instead of a point. Thus, this paper makes use of a polar coordinate representation in any high dimension to randomly generate directions where the concentration can be explicitly controlled, based on which a brand new Gradient Oriented Polar Random Search (GO-POLARS) is designed and proved to satisfy the conditions for strong local convergence.