Oliveira, JosenaldeOliveira, PauloBoaventura-Cunha, JoséPinho, TatianaNonlinear DynamicsOliveira, J.; Oliveira, P.M.; Boaventura-Cunha, J.; Pinho, T. Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator. Nonlinear Dyn. 2017, 90, 1353-1362. ...
Mirjalili S, Mirjalili SM, Lewis A (2014) Let a biogeography-based optimizer train your multi-layer perceptron. Inf Sci 269:188–209. doi:10.1016/j.ins.2014.01.038 Gandomi A, Yang X-S, Talatahari S, Alavi A (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18:89–...
Kumar A, Misra RK, Singh D (2017) Improving the local search capability of Effective Butterfly Optimizer using Covariance Matrix Adapted Retreat Phase. In: 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5–8, 2017. IEEE, pp 1835–1842 Kumar C...
The main purpose of the paper is to demonstrate how the GA optimizer can be improved by incorporating a hybridization strategy. Experimental studies revealed that the hybrid chaotic approach with genetic algorithm (CGA) procedure could produce much more accurate estimates of the true optimum points ...
A quantum particle swarm optimizer with chaotic mutation operator Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques... LDS Coelho - 《Chaos Solitons & Fractals》 被引量: 368发表: 2008年 A quantum ...
Chaos-mutation-based particle swarm optimizer for dynamic environment. In Proceedings of the 3rd Int. Conf. on Intelligent System and Knowledge Engineering; 2008. p. 1032-1037.Dong, D.M.; Jie, J.; Zeng, J.C.; Wang, M. Chaos-mutation-based particle swarm optimizer for dynamic environment....
The paper presents a modified Particle Swarm Optimization (PSO) for the dynamic environment. The modified method provides a detected position for each particle, and applies the detected positions of some randomly sampled particles in the swarm to detect the dynamic change of the environment. If the...
The trajectory planning experimental results show that compared with the antlion optimizer(ALO) algorithm, grey wolf optimizer(GWO), particle swarm optimization(PSO) and artificial bee colony(ABC) algorithm, it has advantages in speed and accuracy to obtain a specific path, and it is of great ...
Therefore, in order to overcome the computational overhead and convergence problems of the multilevel thresholding process, a robust optimizer, namely the Levy flight and Chaos theory-based Gravitational Search Algorithm (LCGSA), is employed to perform the segmentation of the COVID-19 chest CT ...
Additionally, the PCA-GWO (Grey Wolf Optimizer)-SVR and PCA-CPSO-SVR models are compared to assess the effects of GWO and CPSO techniques. Significant improvements were observed when comparing CPSO-SVR with other algorithms. Prediction efficiency was evaluated using mean absolute error (MAE), ...