An enhanced hybrid arithmetic optimization algorithm for engineering applications

TL;DR: Zhang et al. as mentioned in this paper proposed an adaptive hybrid dandelion optimizer called DETDO by combining three strategies of adaptive tent chaotic mapping, differential evolution (DE) strategy, and adaptive t-distribution perturbation to address the shortcomings of weak DO development, easy to fall into local optimum and slow convergence speed.

TL;DR: This study introduces Electric Eel Foraging Optimization (EEFO), a bio-inspired algorithm that mimics electric eel foraging behaviors to optimize complex problems, outperforming 12 other algorithms on 87% of 23 test functions and demonstrating exceptional performance in real-world engineering applications.

TL;DR: A novel optimization algorithm, Quadratic Interpolation Optimization (QIO), is proposed, leveraging generalized quadratic interpolation to tackle numerical optimization and engineering problems, outperforming 12 other optimizers on 23 benchmark functions and 10 engineering problems.

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.

TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.