Multi-objective Moth-Flame Optimization Algorithm for Engineering Problems

Abstract

The initial Moth-Flame Optimization (MFO) algorithm is appropriately changed to deal with multi-objective optimization problems described as Multi-objective Moth-Flame Optimization (MMFO). The primary idea of this MFO is the navigating technique of moths in nature which fly in night time by keeping a fixed angle relative to the moon. Nevertheless, these elegant bugs are entrapped in a useless/deadly spiral route around fabricated lights. Normally principles like using fixed-sized external archive make the recommended technique vary from the initial single-objective MFO. The efficiency of recommended MMFO is tested on eight multi-objective engineering design problems concerning remarkable precision and uniformity compared to Multi-objective Particle Swarm Optimization (MOPSO), Multi-objective Gray Wolf Optimizer (MOGWO), and Multi-objective Ant Lion Optimizer (MOALO). According to the results of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the proposed algorithm can provide quality Pareto fronts with very competitive results.