An improved grey wolf optimizer with hyperbolic tangent updating mechanism for solving optimization problems

The original algorithm of Grey Wolf Optimizer (GWO) has a common problem which is too soon to trap in local optima. This paper presents the Improved Grey Wolf Optimizer (IGWO) by modifying the updating mechanism of the original GWO. The main idea of the new improvement is by introducing a nonlinear updating mechanism based on the hyperbolic tangent function to improve the efficiency of the exploration and the exploitation phase and to decrease the probability of trapping in local optima. The effectiveness of the new approach is evaluated on 30 well-known benchmark functions, and the results are compared with the original GWO. The preliminary findings show that the IGWO algorithm is able to obtain very competitive results in terms of objective functions minimization compared to original GWO algorithms

A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

ABSTRACTThe current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating the nonlinear position-updated mechanism of NSCA for enhanced exploration/exploitation competencies and proficiency of SED in maximizing stagnation avoidance within the local optima. Named the NSCA-SED algorithm, the applicability of the proposed method is assessed by scholastic adoption of a sixth-order numerical transfer function towards two independent high-order systems enclosing Double-Pendulum Overhead Crane and Flexible Manipulator. Experimentation results further suggested NSCA-SED as the superior alternative in terms of execution robustness and consistency excellence against other available optimization-based methods for tackling model order reduction. Exemplified simulations sequentially demonstrated considerable improvements by the employment of NSCA-SED over conventional SCA following respective enhanced proportions of 97.17%, 13.17% and 29.03% for Example 1, Example 2 and Example 3

Levy Flight Safe Experimentation Dynamics Algorithm for Data-Based PID Tuning of Flexible Joint Robot

This paper proposes the data-based PID controller of flexible joint robot based on Levy Flight Safe Experimentation Dynamics (LFSED) algorithm. The LFSED algorithm is an enhanced version of SED algorithm where the random perturbation of the updated tuning variable is based on Levy Flight function. By adopting the Levy Flight term to the updated equation of SED, it is expected that a more efficient searching can be performed than the uniform distribution random numbers. The effectiveness of the LFSED algorithm is verified to tune the PID controller of flexible joint robot. In this flexible joint control problem, two PID controllers are utilized to control both rotary angle tracking and vibration of flexible joint robot. The performance of the proposed data-based PID controller is assessed in terms of trajectory tracking of angular motion, vibration reduction and statistical analysis of the predefined control objective function. The simulation results showed that the data-based PID controller based on LFSED is able to produce better control accuracy than the conventional LFSED based method

Universal Impulse Noise Suppression Using Extended Efficient Nonparametric Switching Median Filter

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance

Promessa tecnológica e vantagem combatente

Para compreender como se vai desde promessas tecnológicas presentes ou futuras até a disponibilidade, pronta e concreta, de vantagem combatente, este artigo enquadra tal processo como a articulação da lógica política com a gramática dos meios de força. Apresenta-se uma ilustração histórica que busca trazer à mente algumas coisas sobre as quais se pensar