A global optimization method for a large number of variables (variant of Alienor method)
Purpose - To use a-dense curves to allow the transform of a multiple function into a single variable function in order to solve global optimization problems. Design/methodology/approach - Use is made of the established Alienor method which has already been applied to biological and industrial processes. The problems tackled have a number of variables and the chosen optimization method is a variant of the Alienor method. Findings - A new method for solving global optimization problem, called the Alienor method is now the subject of many variants. In this paper, it was found that a new reducing transformation a-dense in R-n was successful in solving this type of problem when associated to a functional depending on a large number of variables. The reducing transformation is very efficient and accurate. Research limitations/implications - This is a variant of the proven Alienor Method which has improved the resolution of global optimization problems. It showed that the reducing transformation has the advantage that a small calculation time is obtained even when the relevant series are slowly increasing. Further development of the method is anticipated. Practical implications - Proved very effective for obtaining the global optimum with good precision and very short calculation time for large numbers of variables. Can be performed on micro-calculators. Originality/value - New variant of proven method. Of interest in solution of concrete problems in biology and industry.
cybernetics optimization techniques