Improving Moth-Flame Optimization Algorithm by using Slime-Mould Algorithm

Abstract

The MFO algorithm is one of the modern optimization algorithms based on swarm intelligence, and the SMA algorithm is also one of the latest algorithms in the same field and has the advantages of fast convergence, high convergence accuracy, robust and robust. In this research paper, we introduce an optimized algorithm for MFO based on the SMA algorithm to get better performance using the features in the two algorithms, and two different algorithms are proposed in this field. The two predicted new algorithms were tested with standard test functions and the results were encouraging compared to the standard algorithms.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…4.2.9. Others Moreover, researchers have hybridized an SMA with a sine cosine algorithm [83], marine predators algorithm [85], particle swarm optimization [97], evolutionary algorithm [98], firefly algorithm [99], gray wolf optimization algorithm [100], gradient-based optimizer [101], quadratic approximation [102], tournament selection [103], artificial neural network [104], moth-flame optimization algorithm [105], pattern search algorithm [106], and support vector regression [107]. These hybrid SMA variants indicated their benefits, such as the good balance between exploration and exploitation capabilities, good convergence speed, ability to avoid premature convergence, and reduced computation time.…”