scholarly journals An improved artificial bee colony algorithm with elite-guided search equations

2017 ◽  
Vol 14 (3) ◽  
pp. 751-767 ◽  
Author(s):  
Zhenxin Du ◽  
Dezhi Han ◽  
Guangzhong Liu ◽  
Jianxin Jia
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Of The Art ◽  
Search Process ◽  
Success Rates ◽  
Solution Quality ◽  
Guided Search ◽  
Bee Colony ◽  
Better Than ◽  
Large Disturbance

ABC_elite, a novel artificial bee colony algorithm with elite-guided search equations, has been put forward recently, with relatively good performance compared with other variants of artificial bee colony (ABC) and some non-ABC methods. However, there still exist some drawbacks in ABC_elite. Firstly, the elite solutions employ the same equation as ordinary solutions in the employed bee phase, which may easily result in low success rates for the elite solutions because of relatively large disturbance amplitudes. Secondly, the exploitation ability of ABC_elite is still insufficient, especially in the latter half of the search process. To further improve the performance of ABC_elite, two novel search equations have been proposed in this paper, the first of which is used in the employed bee phase for elite solutions to exploit valuable information of the current best solution, while the second is used in the onlooker bee phase to enhance the exploitation ability of ABC_elite. In addition, in order to better balance exploitation and exploration, a parameter Po is introduced into the onlooker bee phase to decide which search equation is to be used, the existing search equation of ABC_elite or a new search equation proposed in this paper. By combining the two novel search equations together with the new parameter Po, an improved ABC_elite (IABC_elite) algorithm is proposed. Based on experiments concerning 22 benchmark functions, IABC elite has been compared with some other state-of-the-art ABC variants, showing that IABC_elite performs significantly better than ABC_elite on solution quality, robustness, and convergence speed.

An Improved Method of Artificial Bee Colony Algorithm

2011 ◽  
Vol 101-102 ◽  
pp. 315-319 ◽  
Author(s):  
Xin Jie Wu ◽  
Duo Hao ◽  
Chao Xu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Simulation Experiment ◽  
Optimization Problems ◽  
Local Extremum ◽  
Global Search ◽  
Improved Method ◽  
Object Function ◽  
Bee Colony ◽  
Better Than

The basic artificial bee colony algorithm gets local extremum easily and converges slowly in optimization problems of the multi-object function. In order to enhance the global search ability of basic artificial bee colony algorithm, an improved method of artificial bee colony algorithm is proposed in this paper. The basic idea of this method is as follows: On the basis of traditional artificial bee colony algorithm, the solution vectors that found by each bee colony are recombined after each iteration, then the solution vectors of combinations are evaluated again, thus the best result is found in this iteration. In this way the possibility of sticking at local extremum is reduced. Finally the simulation experiment has been finished. The simulation experiment results have shown that the method proposed in this paper is feasible and effective, it is better than basic artificial bee colony algorithm in the global search ability.

Steady State Load Shedding to Prevent Blackout in the Power System using Artificial Bee Colony Algorithm

2015 ◽  
Vol 74 (1) ◽  
Author(s):  
R. Mageshvaran ◽  
T. Jayabarathi
Keyword(s):  
Steady State ◽  
Power System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Optimal Solution ◽  
Load Shedding ◽  
Solution Quality ◽  
Bee Colony ◽  
The Difference

Real and reactive power deficiencies due to generation and overload contingencies in a power system may decline the system frequency and the system voltage. During these contingencies cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Under such situations load shedding is considered as an emergency control action that is necessary to prevent a blackout in the power system by relieving overload in some parts of the system. The aim of this paper is to minimize the amount of load shed during generation and overload contingencies using a new meta-heuristic optimization algorithm known as artificial bee colony algorithm (ABC). The optimal solution for the problem of steady state load shedding is done by taking squares of the difference between the connected and supplied real and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14, 30, 57, and 118 bus test systems. The applicability of the proposed method is demonstrated by comparison with the other conventional methods reported earlier in terms of solution quality and convergence properties. The comparison shows that the proposed algorithm gives better solutions and can be recommended as one of the optimization algorithms that can be used for optimal load shedding.

A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation

2016 ◽  
Vol 367-368 ◽  
pp. 1012-1044 ◽  
Author(s):  
Laizhong Cui ◽  
Genghui Li ◽  
Qiuzhen Lin ◽  
Zhihua Du ◽  
Weifeng Gao ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Guided Search ◽  
Depth First Search ◽  
Bee Colony

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.

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

2018 ◽  
Vol 10 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Chao Yang ◽  
Lixin Guo
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Orthogonal Experimental Design ◽  
Sea Clutter ◽  
Particle Swarm Optimizer ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct ◽  
Comparison Results ◽  
Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

scholarly journals Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Rodrigo Cuesta ◽  
Fernando Paredes
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Formal Model ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Solution Quality ◽  
Bee Colony ◽  
Unicost Set Covering Problem

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

scholarly journals An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alkın Yurtkuran ◽  
Erdal Emel
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Process ◽  
Acceptance Probability ◽  
Abc Algorithm ◽  
Acceptance Rule ◽  
Bee Colony ◽  
Candidate Solution ◽  
New Variant

The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

scholarly journals Design of intrusion detection system based on improved ABC_elite and BP neural networks

2019 ◽  
Vol 16 (3) ◽  
pp. 773-795
Author(s):  
Letian Duan ◽  
Dezhi Han ◽  
Qiuting Tian
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Detection System ◽  
Local Optimum ◽  
Bp Neural Networks ◽  
Guided Search ◽  
Bee Colony

Intrusion detection is a hot topic in network security. This paper proposes an intrusion detection method based on improved artificial bee colony algorithm with elite-guided search equations (IABC elite) and Backprogation (BP) neural net works. The IABC elite algorithm is based on the depth first search framework and the elite-guided search equations, which enhance the exploitation ability of artificial bee colony algorithm and accelerate the convergence. The IABC elite algorithm is used to optimize the initial weight and threshold value of the BP neural networks, avoiding the BP neural networks falling into a local optimum during the training process and improving the training speed. In this paper, the BP neural networks optimized by IABC elite algorithm is applied to intrusion detection. The simulation on the NSL-KDD dataset shows that the intrusion detection system based on the IABC elite algorithm and the BP neural networks has good classification and high intrusion detection ability.

scholarly journals A Modified Artificial Bee Colony Algorithm forp-Center Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Alkın Yurtkuran ◽  
Erdal Emel
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Of The Art ◽  
Benchmark Problems ◽  
Center Problem ◽  
Exploration And Exploitation ◽  
Abc Algorithm ◽  
Coding Schemes ◽  
Bee Colony ◽  
Bee Colonies

The objective of thep-center problem is to locatep-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve thep-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient.

scholarly journals An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Guanlong Deng ◽  
Hongyong Yang ◽  
Shuning Zhang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Flow Shop ◽  
Flow Time ◽  
Flow Shop Scheduling ◽  
Total Flow ◽  
Total Flow Time ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Better Than

This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.

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