scholarly journals An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem

2016 ◽  
Vol 5 (8) ◽  
pp. 24-28
Author(s):  
Shifat Sharmin ◽  
Tanveer Ahmed ◽  
Mohammad Shafiul
Keyword(s):  
Continuous Function ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Function Optimization ◽  
Empirical Comparison ◽  
Bee Colony

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

Robust PID controller design based on H∞ theory and a novel constrained artificial bee colony algorithm

2016 ◽  
Vol 40 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Vahid Bijani ◽  
Alireza Khosravi
Keyword(s):  
Pid Controller ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Robust Performance ◽  
Bee Colony ◽  
New Strategy ◽  
Pid Controller Design ◽  
High Flexibility ◽  
Setting Parameters

This paper presents a new strategy for tuning the coefficients of a PID controller regarding H∞ robust performance and stability constraints based on the constrained artificial bee colony (CABC) algorithm. First, the issue of tuning the PID controller to follow H∞ specifications are introduced, and the objective function and constraints of the optimization problem are specified. Then, a simple and efficient method of transforming the constrained optimization problem to an unconstrained one is presented, and used within the CABC for optimization. The CABC is one of the most recently introduced optimization algorithms, and has the advantages of strong robustness, fast convergence and high flexibility, with fewer setting parameters. The algorithm is essentially a random and intelligent evolutionary method. This method is also utilized and simulated in several models. The simulation results have been compared with other techniques, which demonstrate the efficiency and superiority of the proposed scheme.

Differential quadrature element model updating of frame structures

2013 ◽  
Vol 228 (7) ◽  
pp. 1094-1107 ◽  
Author(s):  
Laleh Fatahi ◽  
Shapour Moradi
Keyword(s):  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Element Model ◽  
Differential Quadrature ◽  
Design Parameters ◽  
Modal Parameters ◽  
Bee Colony ◽  
Differential Quadrature Element Method ◽  
Quadrature Element Method ◽  
Element Method

The following study deals with improving the vibration model of structures obtained by the differential quadrature element method. To this end, first, an initial model of the structure is constructed using the differential quadrature element method in which the values of several physical parameters are unknown. Then, an optimization problem is defined to find the optimum values of the design parameters. In fact, the aim is to minimize an objective function that consists of the weighted sum of the squared errors between the modal parameters (i.e. the natural frequencies, the mode shapes, and the damping ratios) of the differential quadrature element and the experimental models. To solve the optimization problem, a robust evolutionary algorithm, namely the artificial bee colony, is utilized. To verify the effectiveness of the presented approach, the experimental data obtained from the modal testing of a plane frame are utilized to update its differential quadrature element model. The results show that the updating process is successfully performed utilizing artificial bee colony, and the updated differential quadrature element model better represents the vibration behavior of the real structure. Besides, the sensitivities of the eigenvalues of the model with respect to the design parameters are also evaluated to demonstrate the effect of changing the design parameters in the modal parameters of the model.

Sign in / Sign up

Export Citation Format

Share Document