Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode of moth lateral positioning. The basic MFO has no specific, deep strategies in different periods of the algorithm and a fragile evolutionary basis, which may lead to the problem of falling into local optimum and slow convergence trend. Therefore, this paper introduces a double adaptive weight mechanism into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes. The proposed WEMFO adjusts the search strategy adaptively in different periods of the algorithm, making it more flexible between global search (diversification) and local search (intensification). The WEMFO algorithm is compared with some illustrious metaheuristic solvers and advanced metaheuristic methods developed in recent years on thirty benchmark functions. The experimental results expose that the developed WEMFO has apparent compensations in terms of convergence speed and solution accuracy. Moreover, this paper analyzes the diversity and balance of WEMFO and applies the algorithm to several engineering problems. The experimental results show that the WEMFO algorithm has good performance in engineering problems. Additionally, the proposed WEMFO was also applied to train Kernel Extreme Learning Machine (KELM), the resultant optimized WEMFO-KELM model was applied to six clinical disease classification problems. By comparing with MFO-KELM and other five classification models, the experimental results showed that the proposed algorithm had shown better performance in practical problems. An online guide for the algorithm in this research WEMFO and proposed classifier WEMFO-KELM will be publicly available at https://aliasgharheidari.com .

Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis

This paper investigates event-triggered finite-time tracking control problem for full state constraints nonlinear systems with uncertain parameters. Considering a class of full state constraints nonlinear systems, a new finite-time barrier Lyapunov function (FTBLF) is constructed, and it is utilized to achieve finite-time tracking control while each state constraints are not violated. Further, to reduce communication resource burden, a time-varying threshold event-triggered mechanism is proposed. Meanwhile, by integrating prescribed exponential function into FTBLF, the transient performance can be guaranteed and free from influences of event-triggered control input. Finally, on the basic of backstepping design, an event-triggered adaptive finite-time tracking control method is developed. The proposed method guarantees that tracking error tends to a small adjustable set and its trajectory is within specified bound, while full state constraints are never violated. Two examples are given to demonstrate the control effect.

Event-triggered adaptive finite-time tracking control for full state constraints nonlinear systems with parameter uncertainties and given transient performance

In the current investigation, numerical method (CVFEM) was selected to show the impact of Lorentz force on transportation of operate fluid (water with a mixture of Fe3O4 and CuO). Adding the source terms associated with MHD (magneto-hydrodynamic) and porous zone leads to a mathematical model and utilizing vorticity formulation can make the equations to simplify. Not only the viscous and heating irreversibility but also permeable and magnetic terms were calculated. Nu and Be have been measured and based on results, two correlations were defined. As permeability and buoyancy effects augment, the velocity of operating fluid augments while lower velocity will be reported if Lorentz forces increases. Sgen,th decreases about 44% with augment of Ha but it augments about 100% with grow of Ra. Velocity augments about 75% with rise of Da while it declines about 50% with the growth of Ha. Intensification of Ha causes Nu to reduce about 7.85% while Be augments about 13.62%. As Da enhances, Be declines about 5.79 while Nu enhances about 49.11%. With the growth of Ra, Be decreases about 49.79% while Nu augments about 392%.

Simulation of MHD impact on nanomaterial irreversibility and convective transportation through a chamber

A new MPPT design using variable step size perturb and observe method for PV system under partially shaded conditions by modified shuffled frog leaping algorithm- SMC controller

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

/pdf/short-term-load-forecasting-using-neural-network-and-1ps1hv6ikk.pdf

Yan hui Liu

Papers

Fuzzy finite-time stable compensation control for a building structural vibration system with actuator failures