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

Abstract

Multi-objective evolutionary algorithms have shown to be efficiently applied on a variety of benchmarks and in real-world multi-objective optimisation issues. However, MOEAs may encounter difficulties when attempting to solve massive data optimization issues involving hundreds of variables. The initial circumstance introduces a distinctive approach that utilizes solving a single-objective gravitation search problem using the multiple goals GSA method optimization issue for an input parameter range ranging from . This review provides three possible scenarios: a simulation, a multi-objective gravitational search algorithm, and a single objective gravity search method. Although there is no nearby inquiry mechanism, search is Streng subsequently ed because of the large diversity and simple configuration. The three-part evaluation method used to assess MOGSA includes (1) describing the algorithm's benchmarking (unrestricted) to ascertain its effectiveness, (2) evaluating the effectiveness of the method through using average and standard deviations, and MOGS point, and (3) assessing the algorithm itself. The results and explanation of optimizations support the assertion that the MOGSA algorithm competes favourably with cutting-edge meta-heuristic and conventional methods. The previously Meta-Heuristic, BAT, TOPSIS, and GSA computations are the primary concepts.