title: A general CPL-AdS methodology for fixing dynamic parameters in dual environments.
abstract: The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is one bottleneck for this CPL-AdS strategy which is that, when the dimension of the feature, or the number of divided subintervals for each iteration, d is large, the decision table for elimination process is almost unavailable. On the other hand, the larger dimension of the features d can generally make this CPL-AdS strategy avoid oscillation and converge faster. This paper presents a generalized universal decision formula to solve this bottleneck problem. As a matter of fact, this decision formula has a wider usage beyond handling out this SPL problems, such as dealing with deterministic point location problems and searching data in Single Instruction Stream-Multiple Data Stream based on Concurrent Read and Exclusive Write parallel computer model. Meanwhile, we generalized the CPL-AdS strategy with an extending formula, which is capable of tracking an unknown dynamic parameter λ in both informative and deceptive environments. Furthermore, we employed different learning automata in the generalized CPL-AdS method to find out if faster learning algorithm will lead to better realization of the generalized CPL-AdS method. All of these aforementioned contributions are vitally important whether in theory or in practical applications. Finally, extensive experiments show that our proposed approaches are efficient and feasible.
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China. dshuang@tongji.edu.cn
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Wen
authors.initials: W
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*