SOLVING SINGLE OBJECTIVE PRODUCTION PLANNING PROBLEM BASED ON MOGSA ALGORITHM AND TOPSIS TECHNIQUE
On various benchmarks and real-world multi-objective optimisation concerns, multi-objective evolutionary algorithms have proven to be well implemented. However, MOEAs could have several trouble solving thousands of variables for large data optimization problems. The first scenario presents a new technique based on the multi-objective gravity search algorithm GSA, to resolve a single-objective optimisation problem based on the parameter 0 to 1 in order to fix the problem this survey suggests the scenario: a single objective gravity search algorithm, multi-objective gravitational search algorithm and a simulation. The absence of a nearby inquiry system raises the strengthening of search, although the diversity remains high and easily configured. MOGSA is evaluated using the three-part evaluation technique (1) describesMOGSA's benchmarking (unconstrained), in order to determine the algorithm's efficiency, (3) evaluate the algorithm performance using mean, standard deviations, point of the MOGS, and (3) evaluate the algorithm. The findings and the discussion of optimizations confirm that the MOGSA algorithm is competing well with state-of-the-art meta-heuristic and standard approaches. Main Words: algorithms for Meta-Heuristic, BAT, TOPSIS and GSA algorithms.