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.

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION

2014 ◽  
Vol 13 (02) ◽  
pp. 1450010
Author(s):  
MD. SHAFIUL ALAM ◽  
MD. MONIRUL ISLAM ◽  
KAZUYUKI MURASE
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Local Optima ◽  
Selection Scheme ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Approach

The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

Artificial Bee Colony Algorithm to Solve Dynamic Economic Dispatch Considering Wind Power Penetration

2013 ◽  
Vol 416-417 ◽  
pp. 2092-2096
Author(s):  
Xi He ◽  
Gao Xia Wang
Keyword(s):  
Wind Power ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Integrated System ◽  
Swarm Optimization ◽  
Abc Algorithm ◽  
Reasonable Time ◽  
Bee Colony ◽  
Dynamic Economic Dispatch

This paper use artificial bee colony algorithm (ABC) to solve dynamic economic dispatch (DED) problem in wind power integrated system for generating units with value-point effect and system-related constrains. The feasibility of the proposed method is validated with ten-unit-test systems for a period of 6 and 24 hours respectively. The effectiveness and feasibility of the artificial bee colony algorithm are demonstrated by comparing its performance with improved particle swarm optimization. Numerical results show that the ABC algorithm can provide accurate dispatch solutions within reasonable time for certain type of fuel cost functions.

scholarly journals Using bees algorithms for solution of radar pavement monitoring inverse problem

2014 ◽  
Vol 15 (1) ◽  
pp. 53-66
Author(s):  
Alexander Krainyukov ◽  
Valery Kutev ◽  
Elena Andreeva
Keyword(s):  
Inverse Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Physical Characteristics ◽  
Frequency Range ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Pavement Monitoring ◽  
Tree Models ◽  
The Impact

Abstract This work has focused on using of Bee Algorithm and Artificial Bee Colony algorithm for solution the inverse problem of subsurface radar probing in frequency domain. Bees Algorithms are used to minimize the aim function. Tree models of road constructions and their characteristics have been used for solution of the subsurface radar probing inverse problem. There has been investigated the convergence of BA and ABC algorithms at minimisation of the aim function of the inverse problem of radar subsurface probing of roadway structures. There has been investigated the impact of free arguments of BA and ABC algorithm, width of the frequency range and width of the searching interval on the error of reconstruction of electro-physical characteristics of layers and duration of algorithm operating. There has been investigated the impact of electro-physical characteristics of roadway structure layers and width of the frequency range on aim function of radar pavement monitoring inverse problem.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

scholarly journals 15-Puzzle Problem Solving with the Artificial Bee Colony Algorithm Based on Pattern Database

2021 ◽  
Vol 27 (6) ◽  
pp. 635-645
Author(s):  
Adem Tuncer
Keyword(s):  
Artificial Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Algorithms ◽  
Manhattan Distance ◽  
Small Scale ◽  
Heuristic Function ◽  
New Approach ◽  
Abc Algorithm ◽  
Bee Colony

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.

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.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

2020 ◽  
Vol 19 (02) ◽  
pp. 561-600
Author(s):  
Selcuk Aslan
Keyword(s):  
Big Data ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Abc Algorithm ◽  
Bee Colony ◽  
De Algorithm ◽  
Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

scholarly journals A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

2020 ◽  
Vol 10 (10) ◽  
pp. 3352
Author(s):  
Xiaodong Ruan ◽  
Jiaming Wang ◽  
Xu Zhang ◽  
Weiting Liu ◽  
Xin Fu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Numerical Test ◽  
Stochastic Algorithm ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Novel Algorithm

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

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