MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

The moth-flame optimization (MFO) algorithm is an effective nature-inspired based on the chemical effect of light moths as animal with bilateral symmetry. Although it widely used to solve different problems, its movement strategy affects convergence and balance between exploration exploitation when dealing complex problems. Since strategies significantly affect performance algorithms, use multi-search can enhance their ability effectiveness In this paper, we propose a multi-trial vector-based (MTV-MFO) algorithm. proposed algorithm, MFO substituted by vector (MTV) approach combination strategies, each which adjusted accomplish particular behavior. MTV-MFO uses three search global ability, maintain exploitation, prevent original MFO’s premature during process. Furthermore, knowledge inferior preserved in two archives avoid local optima. was evaluated using 29 benchmark problems taken from CEC 2018 competition real parameter optimization. gained results were compared eight metaheuristic algorithms. comparison shows that able provide competitive superior algorithms terms accuracy rate. Moreover, statistical analysis other conducted, our also demonstrated experimentally.