AGTO – SMA: A Hybrid Swarm Intelligence Optimization Algorithm

Ayad  Hamad Khalaf

Ayad Hamad Khalaf Tikrit University, College of Basic Education, Department of Mathematics

Keywords: Hybrid, Optimization, Heuristic Algorithm, Swarm Intelligence, Intelligent Technologies

Abstract

In this research, two types of intelligent algorithms were studied, namely the slime mould algorithm (SMA), which is a meta-heuristics intelligent algorithm that is a distinctive mathematical model, and the Artificial Gorilla Troop Optimization (AGTO), which is a meta-heuristics intelligent swarm algorithm whose model is based on exploitation and exploration. An improved algorithm called AGTO-SMA is a hybrid algorithm that uses two types of swarm intelligence and exploits the distinctive characteristics of each of them, where the two algorithms work together to produce an adequate mathematical model that can avoid falling into local solutions, as well as the speed of finding solutions with a minimum number of iterations and reaching the global optimal solution. In this study, an important fact that has not been highlighted by previous studies is that despite the progress in finding intelligent algorithms, they still fall short of achieving the optimal solution for most of the functions used in applications, and the issue of accuracy in finding solutions has not been discussed. Through the numerical results of the developed algorithm AGTO-SMA, this algorithm showed its superiority over other algorithms individually, its access to the optimal solution and the possibility of overcoming local solutions. Its approach speed was very high. It does not need a large number of elements of the swarm used for the algorithm if compared to its predecessors, which makes it an algorithm of great value in the field of optimization.

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