Simulated Tornado Optimization

TL;DR

Simulated Tornado Optimization is a swarm-based algorithm inspired by tornado air currents, balancing exploration and exploitation with low computational complexity, and showing competitive performance on benchmark functions.

Contribution

Introduces a novel tornado-inspired swarm optimization algorithm with a single adjustable parameter and low complexity, outperforming some existing metaheuristics.

Findings

Achieves comparable or better results than other metaheuristics on benchmarks.

Features a single parameter, tornado diameter, adjustable via randomization.

Maintains low computational complexity due to single search direction per particle.

Abstract

We propose a swarm-based optimization algorithm inspired by air currents of a tornado. Two main air currents - spiral and updraft - are mimicked. Spiral motion is designed for exploration of new search areas and updraft movements is deployed for exploitation of a promising candidate solution. Assignment of just one search direction to each particle at each iteration, leads to low computational complexity of the proposed algorithm respect to the conventional algorithms. Regardless of the step size parameters, the only parameter of the proposed algorithm, called tornado diameter, can be efficiently adjusted by randomization. Numerical results over six different benchmark cost functions indicate comparable and, in some cases, better performance of the proposed algorithm respect to some other metaheuristics.