An augmented animal migration optimization algorithm using worst solution elimination approach in the backdrop of differential evolution

Abstract

A new algorithm has been formulated based on the basic animal migration optimization (AMO) algorithm. During the course of this proposed work it was revealed for the first time that AMO algorithm is a true replica of DEGL algorithm and when mathematical analysis was carried out the similarities were brought to notice which was till date not reported. Further during the investigation it was also witnessed that AMO algorithm in its virgin form is capable of delivering a competitive performance when applied to optimization of non-linear functions referring to CEC 2014 test suite. Such successful achievements using basic AMO algorithm inspired the present authors to take it as a challenge for exploring the possibility of improving the conventional AMO algorithm with an objective of providing it with a new shape and build an efficient framework for tackling the handicaps encountered in original AMO algorithm. In fact the incessant quest of modifying the basic algorithm gave birth to a new algorithm known as augmented animal migration optimization algorithm in the backdrop of differential evolution (AAMO-DE). The proposed algorithm even though incorporated the philosophy of Jaya algorithm,it created a memory hierarchy of worst solutions generated in each iteration. And unlike Jaya algorithm where the position update equation employ the current worst solution the proposed one picks up a random worst solution from the archive to achieve better diversity and also drops the global best term which often yields biased solutions. The proposed AAMO-DE algorithm could accomplish highly encouraging results when CEC 2014 test suite problems were subjected to validation check. The performance of the proposed algorithm was truly impressive in comparison with its counterparts comprising of state-of-the-art algorithms. In case of application to real world engineering problems the outcomes were very promising and really proves that the proposed AAMO-DE algorithm is not only a strong contender in the optimization community exhibiting excellent results but is also a potentially robust algorithm and has the ability to converge towards global optima without being trapped in local minima as evident from test examples.