Using TOPSIS Technology to Solve Multi-Objective Gravitational Search Algorithm Based on Supply Planning

Abstract

 Multi-objective evolutionary (MOE) algorithms have encountered difficulties when calculating big data improvement issues. Initial conditions are multiple pre-existing conditions that contribute to a particular situation. We propose a novel procedure for computing the single goal gravity issue using multi-objective gravity search. GSA is an evolutionary algorithm derived and designed derived from the rule of attraction and weight, and represents a system of elements where each element gives rise to other components by applying a gravitational force. The study aims to achieve the best solution to the optimization problem for input parameters ranging from 0 to 1. The review presents three scenarios: Simulation, a MOGSA, and a single goal gravity search method. The three-part evaluation method, MOGSA, is an extension of GSA designed to address process optimization problems. While GSA identifies a single optimal solution, MOGSA seeks multiple solutions. This paper includes: 1) A description of the algorithm's criteria for validating its effectiveness, 2) An evaluation of the method's effectiveness using means and standard deviations, and 3) An evaluation of the algorithm itself. The results and improvements validate that MOGSA effectively competes with the latest technology analytical together with traditional methods.