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

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.

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Shuffled frog leaping algorithm based on quantum rotation angle

MATEC Web of Conferences ◽

10.1051/matecconf/202030903012 ◽

2020 ◽

Vol 309 ◽

pp. 03012 ◽

Cited By ~ 1

Author(s):

Bibo Hu

Keyword(s):

Artificial Intelligence ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Rotation Angle ◽

Local Area ◽

Convergence Speed ◽

Shuffled Frog Leaping Algorithm ◽

Bee Colony ◽

Shuffled Frog Leaping ◽

Artificial Intelligence Algorithm

In this paper, through the analysis of the artificial intelligence algorithm, shuffled frog leaping algorithm is effectively improved, and the position of the frog is determined by the quantum rotation angle, so as to improve the performance of the algorithm. Compared with the artificial bee colony algorithm and the shuffled frog leaping algorithm, the improved algorithm has a significant improvement in the convergence speed of the algorithm and the ability to jump out of the local area.

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Artificial Bee Colony Algorithm with Time-Varying Strategy

Discrete Dynamics in Nature and Society ◽

10.1155/2015/674595 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 4

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.416-417.2092 ◽

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.

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Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

International Journal of Aerospace Engineering ◽

10.1155/2015/801874 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 18

Author(s):

Li Ding ◽

Hongtao Wu ◽

Yu Yao

Keyword(s):

System Identification ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

State Space Model ◽

Global Optimum ◽

Small Scale ◽

Local Optimum ◽

Unmanned Helicopter ◽

Bee Colony ◽

Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

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Using bees algorithms for solution of radar pavement monitoring inverse problem

Transport and Telecommunication Journal ◽

10.2478/ttj-2014-0006 ◽

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.

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

Scientific Programming ◽

10.1155/2021/3688300 ◽

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.

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

Applied Sciences ◽

10.3390/app10103352 ◽

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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Differential Operators Embedded Artificial Bee Colony Algorithm

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation ◽

10.4018/978-1-4666-3628-6.ch009 ◽

2013 ◽

pp. 149-163

Author(s):

Tarun Kumar Sharma ◽

Millie Pant

Keyword(s):

Global Optimization ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Differential Operators ◽

Optimization Problems ◽

Numerical Results ◽

Benchmark Problems ◽

Continuous Space ◽

Abc Algorithm ◽

Bee Colony

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

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Improved Artificial Bee Colony Algorithm and its Application in Classification

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2018.p0921 ◽

2018 ◽

Vol 30 (6) ◽

pp. 921-926

Author(s):

Haiquan Wang ◽

Jianhua Wei ◽

Shengjun Wen ◽

Hongnian Yu ◽

Xiguang Zhang ◽

...

Keyword(s):

Classification Accuracy ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Initial Solution ◽

Solution Set ◽

Exploration And Exploitation ◽

Abc Algorithm ◽

Bee Colony ◽

Parameters Selection ◽

Optimal Feature

For improving the classification accuracy of the classifier, a novel classification methodology based on artificial bee colony algorithm is proposed for optimal feature and SVM parameters selection. In order to balance the ability of exploration and exploitation of traditional ABC algorithm, improvements are introduced for the generation of initial solution set and onlooker bee stage. The proposed algorithm is applied to four datasets with different attribute characteristics from UCI and efficiency of the algorithm is proved from the results.

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