scholarly journals A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Mao ◽  
Heng-you Lan ◽  
Hao-ru Li
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Population Diversity ◽  
Initial Population ◽  
Roulette Wheel Selection ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Random Step ◽  
Poor Population

As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improvedS-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensionsD=20andD=40are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.

scholarly journals An Artificial Bee Colony Algorithm with Random Location Updating

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lijun Sun ◽  
Tianfei Chen ◽  
Qiuwen Zhang
Keyword(s):  
Information Exchange ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Selection Strategy ◽  
Initial Population ◽  
Search Range ◽  
Bee Colony ◽  
Random Location ◽  
Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

Improved Artificial Bee Colony Clustering Algorithm Based on K-Means

2014 ◽  
Vol 556-562 ◽  
pp. 3852-3855 ◽  
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Bee Colony ◽  
The Poor ◽  
Algorithm Stability ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Clustering Center

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. Artificial Bee Colony algorithm based on K-means was introduced in this article, then put forward an improved Artificial Bee Colony algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved algorithm stability of k-means clustering algorithm well, and more effectively improved clustering quality and property.

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.

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.

Research on the Optimization Scheme of Base Station Construction Based on Artificial Bee Colony Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3612-3615
Author(s):  
Wei Ping Wang ◽  
Miao Yang ◽  
Tao Li ◽  
Ying Qin
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Base Station ◽  
Food Sources ◽  
Mobile Service ◽  
Construction Costs ◽  
Bee Colony ◽  
Construction Scheme ◽  
The Individual

Artificial bee colony algorithm is a new meta heuristic bionic algorithm.Each bee can be viewed as an agent in the algorithm.Through the swarm synergies between the individual achieve the effect of swarm intelligence.Based on the analysis on the principle of bees gather honey,the process of solving functional optimization is converted into the process of swarm finding rich food sources,then we can solve the function.With the arrival of 4G Era,Base station construction is the most crucial step of the mobile communication network popularization, its significance and role should not be underestimated.How to realize the optimization of communication base station construction costs has been the research focus of the mobile service operator and colleges and universities.The artificial bee colony algorithm is applied to solve the optimal solution of base station construction scheme in this paper,which is very critical reference for decision in real base station construction.

Population Diversity Guided Dimension Perturbation for Artificial Bee Colony Algorithm

2021 ◽  
pp. 473-485
Author(s):  
Tao Zeng ◽  
Tingyu Ye ◽  
Luqi Zhang ◽  
Minyang Xu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Bee Colony
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