A global optimization method for a large number of variables (variant of Alienor method)

Abstract

PurposeTo use α‐dense curves to allow the transform of a multiple function into a single variable function in order to solve global optimization problems.Design/methodology/approachUse 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.FindingsA 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 α‐dense in Rn 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/implicationsThis 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 implicationsProved 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/valueNew variant of proven method. Of interest in solution of concrete problems in biology and industry.