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Abstract

A method and system for returning an optimum (or near-optimum) solution to a nonlinear programming problem. By specifying a precision coefficient, the user can influence the flexibility of the returned solution. A population of possible solutions is initialized based on input parameters defining the problem. The input parameters may include a minimum progress and a maximum number of iterations having less the minimum progress. The solutions are mapped into a search space that converts a constrained problem into an unconstrained problem. Through multiple iterations, a subset of solutions is selected from the population of solutions, and variation operators are applied to the subset of solutions so that a new population of solutions is initialized and then mapped. If a predetermined number of iterations has been reached, that is if the precision coefficient has been satisfied, the substantially optimum solution is selected from the new population of solutions. The system and method can be used to solve various types of real-world problems in the fields of engineering and operations research.

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