An improved genetic algorithm (IGA) based on a novel selection strategy to handle nonlinear programming problems is proposed. Each individual in selection process is represented as a three-dimensional ...

Second, the conversion to a single-objective optimization problem involves additional constraints. Third, since most real-world goal programming problems involve nonlinear criterion functions, the ...

Learn about the common challenges and limitations of linear and nonlinear programming methods and how to overcome or mitigate them for algorithm design.

Researchers utilise models that blend bilevel optimisation, mixed-integer nonlinear programming (MINLP) and distributed algorithms such as the alternating direction method of multipliers (ADMM) to ...

Nonlinear Programming algorithms cannot consistently find global minima. All the algorithms in PROC NLP find a local minimum for this problem. If you need to check whether the obtained solution is a ...

In addition, two algorithms are presented for approximately solving fractional programming (FP) problems. The first algorithm is based on an objective space cut and bound method for solving convex FP ...

Nonlinear convex programming (NCP) has convex functions for both the objective and constraints, where a convex function is one with a single global minimum or maximum and curves downward or upward ...

A novel nonlinear programming based control allocation scheme is developed. The performance of this nonlinear control allocation algorithm is compared with that of other control allocation approaches, ...

This course continues our data structures and algorithms specialization by focussing on the use of linear and integer programming formulations for solving algorithmic problems that seek optimal ...