Artificial Bee Colony Algorithm with Modified Operators of Determining the Profitable Food Sources for Identification the Models of Atmospheric Pollution by Nitrogen Dioxide

2020 ◽  
Author(s):  
Mykola Dyvak ◽  
Natalia Porplytsya ◽  
Andriy Pukas ◽  
Iryna Voytyuk ◽  
Nazar Huliiev ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Atmospheric Pollution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Food Sources ◽  
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.

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.

Hybridizing Artificial Bee Colony Algorithm with Multi-Parent Crossover Operator

2015 ◽  
Vol 6 (2) ◽  
pp. 18-32 ◽  
Author(s):  
Amal Mahmoud Abunaser ◽  
Sawsan Alshattnawi
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Food Source ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Food Sources ◽  
Mutation Operator ◽  
Test Functions ◽  
Crossover Operator ◽  
Bee Colony ◽  
Intelligent Behavior

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

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