Improved averaging techniques for solving multi-objective optimization (MOO) problems

Abstract

This study evaluates the existing averaging techniques used for solving multi-objective optimization (MOO) problems. The problems are solved using mean, geometric and harmonic mean averaging techniques. The solutions obtained using the existing averaging techniques were not appropriate. Improved averaging techniques using mean, geometric and harmonic mean are proposed in this study. These techniques have been tested with the suitable examples and found superior to existing MOO averaging techniques.

A new Mean Deviation and Advanced Mean Deviation Techniques to Solve Multi-Objective Fractional Programming Problem Via Point-Slopes Formula

“…Gupta, Rani, and Goyal(Gupta et al, 2019) proposed an algorithm with an efficient method to obtain a solution of the multi-objective quadratic fractional optimization model (MOQFOM) with a trapezoidal fuzzy number, used αcut method, although, this idea tried to reduce the error values of the decision. Chandra Sen (Sen, 2020) studied existing averaging techniques and suggested improved averaging techniques applied for solving multi objective optimization (MOO). Hejazi (Hejazi & Nobakhtian, 2020) proposed the idea of convexificators is used to derive the Karush-Kuhn-Tucker conditions at weak efficient solution of MOLFPP, and investigated the relationships between equality and inequality constraints.…”

Improved Weighted Sum Multi Objective Programming

“…The method has been successfully applied for resources use planning in agriculture [1], [2]. Several variants of the formulation of combined objective functions have been proposed [3], [4] for obtaining desirable solution.…”