scholarly journals Solving Parameter Identification of Nonlinear Problems by Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
S. Talatahari ◽  
H. Mohaggeg ◽  
Kh. Najafi ◽  
A. Manafzadeh
Keyword(s):  
Parameter Identification ◽  
Artificial Bee Colony ◽  
Nonlinear Problems ◽  
Optimization Method ◽  
Identification Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Bee Foraging ◽  
Parameter Identification Problems ◽  
Abc Method

A new optimization method based on artificial bee colony (ABC) algorithm is presented for solving parameter identification problems. The ABC algorithm as a swarm intelligent optimization algorithm is inspired by honey bee foraging. In this paper, for the first time, the ABC method is developed to determine the optimum parameters of Bouc-Wen hysteretic systems. The proposed method exhibits efficiency, robustness, and insensitivity to noise-corrupted data. The results of the ABC are compared with those other optimization algorithms from the literature to show the efficiency of this technique for solving parameter identification problems.

scholarly journals Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chao Yang ◽  
Jian-Ke Zhang ◽  
Li-Xin Guo
Keyword(s):  
Convergence Rate ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Research Field ◽  
Invasive Weed ◽  
Local Optima ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct

The artificial bee colony (ABC) algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL) and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC) technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO) and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

scholarly journals Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Luong Le Dinh ◽  
Dieu Vo Ngoc ◽  
Pandian Vasant
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Capacity Limits ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Power ◽  
Line Flow ◽  
Power Flow Problem

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

Implementation of an Artificial Bee Colony to Solve an Order Picking Problem

2019 ◽  
pp. 144-160
Author(s):  
Luis Enrique Cisneros Saucedo ◽  
Julia Patricia Sanchez-Solis ◽  
Francisco López-Ramos ◽  
Jorge Rodas-Osollo
Keyword(s):  
Nonprofit Organizations ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimization Method ◽  
Order Picking ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Limited Budget ◽  
Study Case ◽  
Parameter Values

The artificial bee colony (ABC) algorithm is an optimization method based on swarm intelligence which has demonstrated to be capable of obtaining satisfactory results on a diversity of optimization problems. However, the implementation of this optimization method hasn't been much explored on order picking problems, even though order picking represents up to 55% of the total operational cost of a typical warehouse. The order picking problem has even more importance on nonprofit organizations like food banks since they operate with a limited budget. In this chapter, the authors implemented an ABC algorithm to solve the order picking problem within a food bank. The goal was to determine which parameter values contribute the most during the optimization process. Experiments were conducted using nine sets of parameters for the ABC; results show that the approach is suitable for the study case.

scholarly journals Optimizing the Weight of Truss using Modified Artificial Bee Colony Algorithm

2021 ◽  
pp. 898-904
Author(s):  
Sumit Mamtora ◽  
Vishal Arekar ◽  
Vishalkumar Patel
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Test Problem ◽  
Artificial Bee Colony ◽  
Research Work ◽  
Optimization Method ◽  
Truss Structure ◽  
Matlab Software ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Work Done

In this paper a research work done to optimize the truss structure using modified ABC algorithm. Three test problem were conducted to verify that the modified ABC algorithm is effective in optimizing. MATLAB software used in this work. The result obtained by modified ABC algorithm is compared with other optimization method. The result shows that modified ABC algorithm gives result as good as other methods.

A quasi-affine transformation artificial bee colony algorithm for global optimization

2021 ◽  
pp. 1-18
Author(s):  
Baohua Zhao ◽  
Tien-Wen Sung ◽  
Xin Zhang
Keyword(s):  
Affine Transformation ◽  
Artificial Bee Colony ◽  
Triangular Matrix ◽  
Convergence Speed ◽  
Test Set ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Search Mechanism ◽  
Multiple Dimensions ◽  
Collaborative Search

The artificial bee colony (ABC) algorithm is one of the classical bioinspired swarm-based intelligence algorithms that has strong search ability, because of its special search mechanism, but its development ability is slightly insufficient and its convergence speed is slow. In view of its weak development ability and slow convergence speed, this paper proposes the QABC algorithm in which a new search equation is based on the idea of quasi-affine transformation, which greatly improves the cooperative ability between particles and enhances its exploitability. During the process of location updating, the convergence speed is accelerated by updating multiple dimensions instead of one dimension. Finally, in the overall search framework, a collaborative search matrix is introduced to update the position of particles. The collaborative search matrix is transformed from the lower triangular matrix, which not only ensures the randomness of the search, but also ensures its balance and integrity. To evaluate the performance of the QABC algorithm, CEC2013 test set and CEC2014 test set are used in the experiment. After comparing with the conventional ABC algorithm and some famous ABC variants, QABC algorithm is proved to be superior in efficiency, development ability, and robustness.

Sign in / Sign up

Export Citation Format

Share Document