| Key Words |
One-dimensional optimization; accelerated convergence; dichotomy; double dichotomy; algorithm efficiency; uncertainty interval |
| Abstract |
A new heuristic optimization algorithm with accelerated convergence is proposed for search a maximum of one-dimensional (single variable), unimodal objective functions. The algorithm is a combination of the dichotomy method, the Kiefer–Johnson method, and the fourth grade functional series. A comparative analysis has been made with other known methods and its effectiveness and accelerated convergence have been demonstrated for cases where the uncertainty interval in the search is very large. The efficiency of the algorithm compared to other known algorithms is based on the number of the objective function evaluation to find the optimum with different accuracy requirements for localization the maximum (or minimum) of the function. The method and proposed algorithm is suitable for parameters estimation in mathematical models |
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