Abstract

The transverse-orientation process is at the heart of the new and innovative moth-flame optimization (MFO) technique,which draws inspiration from nature. This device incorporates a specialised form of navigational techniques that are symbolic ofthe direct flight of moths toward the moon at night. Although MFO has been successfully applied to a number of optimizationproblems, it still has the same issues as other evolutionary algorithms, namely a sluggish convergence rate and a lack of globalsearch capabilities. To remedy this, we introduce the Mathematical Operator Based MFO Algorithm (or MOP-MFO for short).There are two stages to the planned MOP-MFO. After the position update phase of the original MFO algorithm, a simple quadraticinterpolation approach is added to accelerate the convergence rate. The non-linear operator is introduced to keep a healthy balancebetween expansion and specialisation. The suggested MOP-MFO, MFO has been put through its paces by being compared to 36traditional benchmark functions picked from the literature, and then statistically tested using a procedure called the Friedman ranktest. Two engineering design challenges have also been solved using the MOP-MFO to demonstrate its problem-solving potential.These findings confirmed that the suggested MOP-MFO out performed competing optimization techniques.Keywords: Moth flame optimization, Evolutionary Algorithms, Quadratic interpolation. Benchmark functions.