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.

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 Artificial Bee Colony Algorithm with Time-Varying Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Quande Qin ◽  
Shi Cheng ◽  
Qingyu Zhang ◽  
Li Li ◽  
Yuhui Shi
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Studies ◽  
Population Based ◽  
Time Varying ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Linear And Nonlinear ◽  
Proper Balance

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

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 A Comprehensive review of Artificial Bee Colony Algorithm

2013 ◽  
Vol 5 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Kamalam Balasubramani ◽  
Karnan Marcus
Keyword(s):  
Foraging Behavior ◽  
Artificial Bee Colony ◽  
Population Based ◽  
Convergence Properties ◽  
Abc Algorithm ◽  
Good Convergence ◽  
Hybrid Approaches ◽  
Constrained Problems ◽  
Bee Colony ◽  
Comprehensive Survey

The Artificial Bee Colony (ABC) algorithm is a stochastic, population-based evolutionary method proposed by Karaboga in the year 2005. ABC algorithm is simple and very flexible when compared to other swarm based algorithms. This method has become very popular and is widely used, because of its good convergence properties. The intelligent foraging behavior of honeybee swarm has been reproduced in ABC.Numerous ABC algorithms were developed based on foraging behavior of honey bees for solving optimization, unconstrained and constrained problems. This paper attempts to provide a comprehensive survey of research on ABC. A system of comparisons and descriptions is used to designate the importance of ABC algorithm, its enhancement, hybrid approaches and applications.

A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

2016 ◽  
Vol 6 (6) ◽  
pp. 2621
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Time Interval ◽  
Ramp Rate ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Dynamic Economic Dispatch

<p>In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods.</p>

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 A Modified ABC Algorithm for Solving Non-Convex Dynamic Economic Dispatch Problems

2016 ◽  
Vol 6 (6) ◽  
pp. 2621
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Optimization Method ◽  
Population Based ◽  
Test System ◽  
Time Interval ◽  
Ramp Rate ◽  
Bee Colony ◽  
Mutation Strategy ◽  
Dynamic Economic Dispatch

<p>In this paper, a modified artificial bee colony (MABC) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. Artificial bee colony algorithm is a recent population-based optimization method which has been successfully used in many complex problems. A new mutation strategy inspired from the differential evolution (DE) is introduced in order to improve the exploitation process. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature. It is shown that the optimum results can be obtained more economically and quickly using the proposed method in comparison with the earlier methods.</p>

scholarly journals Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Tinggui Chen ◽  
Renbin Xiao
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony ◽  
Local Optimum ◽  
Immune Network ◽  
Artificial Immune ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Searching Strategy ◽  
Artificial Immune Network ◽  
Self Adaptive

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

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