scholarly journals MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL

2018 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nursyiva Irsalinda ◽  
Sugiyarto Surono
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Selection Process ◽  
Optimization Technique ◽  
Food Sources ◽  
Abc Algorithm ◽  
Average Value ◽  
Bee Colony ◽  
Objective Function Values ◽  
Better Than

Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

A Novel Variable Population Size Artificial Bee Colony Algorithm with Convergence Analysis for Optimal Parameter Tuning

2017 ◽  
Vol 16 (03) ◽  
pp. 1750018 ◽  
Author(s):  
Nasr Elkhateeb ◽  
Ragia Badr
Keyword(s):  
Convergence Analysis ◽  
Population Size ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Food Sources ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Variable Population ◽  
The Impact ◽  
Variable Population Size

This paper introduces a novel algorithm called variable population size artificial bee colony (VPS-ABC) optimization algorithm. VPS-ABC is proposed to overcome the impact of the effect of initial population and improve the convergence rate of classical ABC. The main idea is based on reducing the number of food sources gradually and moving the bees towards the global best food source in each re-initialization process. Moreover, an analysis for convergence of the ABC algorithm is proofed in details. The convergence analysis is based on the relation between ABC variants and the general solution of the food source regeneration equation. To show the fitness of the proposed algorithm, a comparison is made between VPS-ABC versus classical ABC, PSO, and GA algorithms in tuning the proportional-integral-derivative (PID) controllers. Simulation results show that VPS-ABC algorithm is highly competitive, often outperforming PSO and GA algorithms.

scholarly journals A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhendong Yin ◽  
Xiaohui Liu ◽  
Zhilu Wu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Additive White Gaussian Noise ◽  
Ultra Wideband ◽  
System Capacity ◽  
Food Sources ◽  
Solution Quality ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Multiuser Detector

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

Topology Optimization of Multicell Tubes Under Out-of-Plane Crushing Using a Modified Artificial Bee Colony Algorithm

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Jianguang Fang ◽  
Guangyong Sun ◽  
Na Qiu ◽  
Grant P. Steven ◽  
Qing Li
Keyword(s):  
Topology Optimization ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Food Sources ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Design Variables ◽  
Highly Nonlinear ◽  
Out Of Plane ◽  
Energy Absorbing

Multicell tubal structures have generated increasing interest in engineering design for their excellent energy-absorbing characteristics when crushed through severe plastic deformation. To make more efficient use of the material, topology optimization was introduced to design multicell tubes under normal crushing. The design problem was formulated to maximize the energy absorption while constraining the structural mass. In this research, the presence or absence of inner walls were taken as design variables. To deal with such a highly nonlinear problem, a heuristic design methodology was proposed based on a modified artificial bee colony (ABC) algorithm, in which a constraint-driven mechanism was introduced to determine adjacent food sources for scout bees and neighborhood sources for employed and onlooker bees. The fitness function was customized according to the violation or the satisfaction of the constraints. This modified ABC algorithm was first verified by a square tube with seven design variables and then applied to four other examples with more design variables. The results demonstrated that the proposed heuristic algorithm is capable of handling the topology optimization of multicell tubes under out-of-plane crushing. They also confirmed that the optimized topological designs tend to allocate the material at the corners and around the outer walls. Moreover, the modified ABC algorithm was found to perform better than a genetic algorithm (GA) and traditional ABC in terms of best, worst, and average designs and the probability of obtaining the true optimal topological configuration.

Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

2018 ◽  
Vol 32 (11) ◽  
pp. 1850131 ◽  
Author(s):  
Geng Wang ◽  
Kexin Zhou ◽  
Yeming Zhang
Keyword(s):  
Food Source ◽  
Artificial Bee Colony ◽  
Search Performance ◽  
Hysteresis Model ◽  
Unknown Parameters ◽  
Optimal Position ◽  
Bee Colony ◽  
Standard Particle Swarm Optimization ◽  
Global Exploitation ◽  
Better Than

The widely used Bouc–Wen hysteresis model can be utilized to accurately simulate the voltage–displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc–Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

scholarly journals A Tristage Adaptive Biased Learning for Artificial Bee Colony

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Qiaoyong Jiang ◽  
Yueqi Ma ◽  
Yanyan Lin ◽  
Jianan Cui ◽  
Xinjia Liu ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Learning Strategy ◽  
Population Distribution ◽  
Food Sources ◽  
Search Direction ◽  
Bee Colony ◽  
Benchmark Suite ◽  
Over Exploitation ◽  
Better Than ◽  
Biased Information

In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.

scholarly journals Improved Artificial Bee Colony Algorithm and Its Application in LQR Controller Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Haiquan Wang ◽  
Lei Liao ◽  
Dongyun Wang ◽  
Shengjun Wen ◽  
Mingcong Deng
Keyword(s):  
Artificial Bee Colony ◽  
Linear Quadratic Regulator ◽  
Food Sources ◽  
Linear Quadratic ◽  
Pendulum System ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Inverted Pendulum System ◽  
Fitness Value ◽  
Lqr Controller

In order to get the optimal performance of controller and improve the design efficiency, artificial bee colony (ABC) algorithm as a metaheuristic approach which is inspired by the collective foraging behavior of honey bee swarms is considered for optimal linear quadratic regulator (LQR) design in this paper. Furthermore, for accelerating the convergence speed and enhancing the diversities of population of the traditional ABC algorithm, improved solution searching approach is proposed creatively. The proposed approach refers to the procedure of differential mutation in differential evolutionary (DE) algorithm and produces uniform distributed food sources in employed bee phase to avoid local optimal solution. Meanwhile, during the onlooker bees searching stage where the solution search area has been narrowed by employed bees, new solutions are generated around the solution with higher fitness value to keep the fitness values increasing monotonously. The improved ABC algorithm is applied to the optimization of LQR controller for the circular-rail double inverted pendulum system, and the simulation results show the effect on the proposed optimization problem.

scholarly journals Energy efficient spectrum sensing for cognitive radio network using artificial bee colony algorithm

2018 ◽  
Vol 7 (4) ◽  
pp. 2319 ◽  
Author(s):  
Geoffrey Eappen ◽  
Dr T. Shankar
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Artificial Bee Colony ◽  
Cognitive Radio Network ◽  
Optimization Technique ◽  
Radio Network ◽  
Abc Algorithm ◽  
Bee Colony

In this paper Artificial Bee Colony (ABC) algorithm based optimization of energy efficiency for spectrum sensing in a Cognitive Radio Network (CRN) is implemented. ABC algorithm which is an efficient optimization technique is used for optimizing energy efficiency func-tion derived for cognitive users, where energy efficiency function is derived as the dependency on spectrum sensing time and the transmis-sion power. Energy efficiency optimized by ABC is compared with Particle Swarm Optimization (PSO) based technique. Simulation results shows that with ABC it is able to achieve more energy efficient spectrum sensing as compared to PSO optimized with a margin of 33% efficiency over PSO.  

scholarly journals Handling Optimization Problem, and the Scope of Varied Artificial Bee Colony (ABC) Algorithms: A Contemporary

2019 ◽  
Vol 8 (6S4) ◽  
pp. 607-611
Keyword(s):  
Honey Bees ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Research Community ◽  
Food Sources ◽  
Search Process ◽  
Abc Algorithm ◽  
Success Stories ◽  
Bee Colony ◽  
Successful Search

One of the most successful search algorithms of the last decade is Artificial Bee Colony (ABC) algorithm. It was first coined by Dervis Karaboga, 2005. Since then a group of variants of the algorithm have been anticipated to find solutions for the problems of optimization. The motivation for the algorithm is the search process of honey bees for food sources. The present paper aimed to bring out the evolutionary developments of the algorithm that cover numerous versions of the algorithm with the strategic changes to meet the optimization needs of the adopted problem contexts. This survey clearly reviewed the basic types, advancements, application areas, and the relevance of the ABC algorithm addressing various problem contexts. The efforts made by the research community since the last two decades along with the success stories are discussed in detail. The attachment of the optimization process of ABC with data mining is dealt in particular. Finally the opportunities and the scope of the application of the algorithm in large areas of problem domains are highlighted.

scholarly journals The Artificial Bee Colony Algorithm for Global Optimization of Nanosized Clusters

2019 ◽  
Author(s):  
Jun Zhang ◽  
Vassiliki-Alexandra Glezakou
Keyword(s):  
Global Optimization ◽  
Artificial Bee Colony ◽  
Fundamental Problem ◽  
Complex Structures ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Energy Chemistry ◽  
Nature Gas ◽  
Chemical Fields ◽  
Better Than

Global optimization of nanosized clusters is an important and fundamental problem in theoretical studies in many chemical fields, like catalysis, material, or energy chemistry, etc. In this paper, the powerful artificial bee colony (ABC) algorithm, which has been applied successfully in the global optimization of atomic and molecular clusters, has been developed for nanosized clusters of complex structures. The new ABC algorithm is applied to the global optimization of 4 systems of different chemical nature: gas phase Au55, ligated Au82+, graphene oxide and defected rutile-supported Au8, and cluster assemble [Co6Te8(PEt3)6][C60]𝑛. These clusters have sizes that lie between 1 to 3 nm and contain up to 1000 atoms, raising great challenges to the algorithm. Reliable global minima (GMs) are obtained for all cases, some of which are better than those reported in literature, indicating the excellent perfor-mance of the new ABC algorithm. These GMs provide chemically important insights into the systems. The new ABC algorithm has been coded into the latest version of ABCluster, making it a convenient and powerful tool for chemists from broad fields to rapidly carry out global optimizations of nanosized clusters.

scholarly journals Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gan Yu ◽  
Hongzhi Zhou ◽  
Hui Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Convergence Speed ◽  
One Dimension ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Reduction Strategy ◽  
One Dimensional ◽  
Bee Colony

To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

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