Multi-tracker Optimization Algorithm: A General Algorithm for Solving Engineering Optimization Problems

TL;DR: This paper proposes an adaptive robust control scheme for industrial robots using a novel integral terminal fractional-order super-twisting algorithm and radial basis function neural networks to enhance accuracy and reduce chattering under uncertainties.

Abstract: The novelty of this research lies in presenting a fresh stochastic algorithm enthused via ‘Velociraptor’ social intelligence in wildlife (or nature), known as the Velociraptor Group Optimization algorithm (VROA). In this strategy, ‘Velociraptor’ co-operative natural life cycle is mathematically framed, and novel mechanisms are presented to perform search (or exploration) and hunting (or exploitation). This suggests that the proposed method reveals a noteworthy ability in both exploitation and exploration. Furthermore, it successfully stabilities exploration and exploitation, supporting the search process. In the direction of evaluating the VROA, we utilized it on 51 CEC'17, CEC'20, and CEC'22 standard benchmark suites and six multidisciplinary engineering optimization functions. Furthermore, well-known statistical methods like Wilcoxon rank-sum test and Friedman's test have been used to verify the strength of the proposed algorithm against various optimizers. The tabulated numerical and statistical solutions show that VROA performs better than recent optimizers on most standard test suites and has efficiently attained the most competent resolution while concurrently upholding adherence to the designated constraints. The results validate that VROA can offer efficient and accurate optimal solutions in evaluation with recent metaheuristics.

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.

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 optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.

TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.