Artificial Bee Colony Programming Research Articles

Immune Plasma Programming: A new evolutionary computation-based automatic programming method

Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems.

Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade

Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are utilized for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time.

Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete

The use of recycled aggregate concrete (RAC) in the construction industry can help to prevent irreparable environmental damages and to mitigate the depletion rate of natural resources. However, the quality of the RAC should be investigated before its practical applications. Compressive strength of the RAC (fRAC′) is one of the most crucial design parameters, which is measured by time-consuming and cost-extensive experiments. One solution to restrict the number of experiments and achieve reliable fRAC′ estimation is through employing machine learning methods, Artificial Bee Colony Expression Programming (ABCEP) is a newly proposed automatic regression technique that is used in this study to predict the fRAC′. For comparison purposes, four extensions of artificial bee colony programming techniques (i.e., Artificial Bee Colony Programming (ABCP), quick Artificial Bee Colony Programming (qABCP), Quick semantic Artificial Bee Colony Programming (qsABCP), and Semantic Artificial Bee Colony Programming (sABCP)) were also served. To analyze the results, the average and best performances of all algorithms, regression analysis, execute run times, Wilcoxon signed-rank test, and the behavior of algorithms dealing with the local optima were investigated. The results show that the ABCEP method is the most effective technique, with the average root mean squared error of 10.36 MPa compared to 16.23 MPa, 10.82 MPa, 17.71 MPa, and 14.20 MPa for the developed ABCP-, qABCP-, qsABCP-, and sABCP-based models, respectively, in colony size of 30. In addition, the run time of this algorithm is remarkably less than the other algorithms.

Solving Symmetrical Drop Suspended Equilibrium Equation by Artificial Bee Colony Programming

Solving Symmetrical Drop Suspended Equilibrium Equation by Artificial Bee Colony Programming

Estimating The Aeration Performance Of Venturi-Conduit By Artificial Bee Colony Programming

Oxygen concentration dissolved in water is an important parameter used to measure the quality of water. Increasing the concentration of oxygen by transferring oxygen from the atmosphere is named as aeration. Aeration can be performed with many hydraulic structures. Two of the structures that have become widespread in the field of hydraulics are gated conduit and venturi. Venturi has throat part and air hole located into throat part. A difference of pressure, which provides aeration occurs between the venturi inlet and the throat part. A pressure difference occurs between up and down flows of the door in a partially opened gated conduit. Air entrains into flow from an air vent that were drilled downstream of the gate. A venturi was placed on air hole of a circular conduit that as called venturi-conduit, a new aeration system. The basic steps of the Artificial Bee Colony Programming (ABCP) developed for the solution of the symbolic regression problem come from the Artificial Bee Colony (ABC). In this study, ABCP is proposed to obtain functions that can model the ventilation performance of conduits with venture-conduit. The results of the method trained with experimentally measured data on 2 ventures with different contraction ratios were compared with artificial neural networks and genetic programming. ABCP outperformed genetic programming and neural networks on test data with an R2 value of 0.99 in both datasets. The RMSE values of 1.64 and 2.66, respectively, in the 2 data sets indicate that ABCP is capable of generating the appropriate function for the problem.

A new method based on artificial bee colony programming for the regional standardized intensity–duration‒frequency relationship

A new method based on artificial bee colony programming for the regional standardized intensity–duration‒frequency relationship

A prediction model for safety performance of construction sites using a linear artificial bee colony programming approach

The reputation of the construction industry as one of the most world’s dangerous industries is because of its tremendous accident and fatality rate. In this study, a prediction model for construction projects’ safety performance was presented as an alarm system. The proposed model can predict the safety performance at the beginning of the project. To do so, firstly, data preparation and, secondly, developing an automatic regression model were carried out as the two main steps of the study. To collect data, after identifying effective criteria, a questionnaire was developed. Next, an innovative machine learning method, named linear artificial bee colony programming, was used to discover the relationship between the identified criteria and safety performance. The results of one-dimensional and two-dimensional sensitivity analysis show that the four criteria of management commitment to safety, safety training, safety team and safety budget are the most important factors that influence safety performance.

Variable neighborhood programming for symbolic regression

In the field of automatic programming (AP), the solution of a problem is a program, which is usually represented by an AP-tree. A tree is built using functional and terminal nodes. For solving AP problems, we propose a new neighborhood structure that adapts the classical “elementary tree transformation” (ETT) into this specific AP-tree. The ETT is the process of removing an edge and adding another one to obtain a new feasible tree. Experimental comparison with reduced VNP, i.e., with VNP without local search, genetic programming, and artificial bee colony programming shows clearly advantages of the new proposed BVNP method, in terms of speed of convergence and computational stability.

Introduction of ABCEP as an automatic programming method

Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of artificial bee colony programming, such as weak convergence and high locality. A total number of 15 real-world regression benchmark functions was used to evaluate the performance of the proposed model. For comparison purposes, successful run percentage, mean best cost, convergence performance, and run time of ABCEP were compared to those of other tested automatic programming algorithms, including artificial bee colony programming, gene expression programming, genetic programming, and quick artificial bee colony programming. A Wilcoxon signed-rank test was also done to compare the behavior of the algorithms. Additionally, the accuracy of all algorithms was then evaluated using three real-world practical benchmarks. The results indicate that the predictions generated by ABCEP are better than those obtained by other control algorithms based on successful runs, mean fitness values, and convergence rate.

Solving artificial ant problem using two artificial bee colony programming versions

Artificialant problem is considered as a sub-problem of robotic path planning. In this study, it is solved using two different methods: artificial bee colony programming and a new version of it called shrinking artificial bee colony programming. The former is a novel evolutionary computation based automatic programming method based on artificial bee colony algorithm and it was previously applied to this problem by the researchers. However, in this study, more comprehensive analyses and comparison study are provided. The shrinking artificial bee colony programming was developed in this study and its basic idea is to reduce the number of food sources, periodically, instead of a constant number used in the artificial bee colony programming. First, some parameter tuning studies were carried out for the shrinking artificial bee colony programming. Then, performances of the artificial bee colony programming, shrinking artificial bee colony programming and some other evolutionary computation based automatic programming methods were compared on Santa Fe and Los Altos Hills trails. Simulation results and the comparison study show that both of the algorithms can be used to solve the artificial ant problem effectively. Furthermore, the periodically decreasing population size property added to the artificial bee colony programming improves the performance of the algorithm on the artificial ant problem. While the proposed approach shows one of the superior performances among the considered methods, the results of the artificial bee colony programming are competitive to the methods in the comparison study.

Solving differential equations with artificial bee colony programming

Relying on artificial bee colony programming (ABCP), we present in this paper, for the first time, a novel methodology for solving differential equations. The three-phase evolving process of ABCP is managed to apply on the issue of recovering the exact solution of differential equations through a well-posed problem. In fact, the original ABCP model which has been initially developed for symbolic regression cannot be used directly as differential problems might have multiple outputs. Moreover, the definition of fitness function is a critical problem-dependent issue for model design. In this sense, a problem-specific ABCP algorithm is worked out in the present contribution. With the proposed algorithm, solution with multiple outputs can evolve under a multiple-tree framework toward the exact solution. For fitness function evaluation, different forms are derived for ordinary and partial differential equations by performing experiments with multiple runs. Results on several differential equations are reported and compared to other advanced methods to assess the feasibility and the potential of the proposed method. A computational performance evaluation is provided for the considered examples and completed with an additional study on the impact of key control parameters.

Feature selection and classification of metabolomics data using artificial bee colony programming (ABCP)

One area of metabolic data analysis is processes that involve the detection and discovery of biomarkers used in the early diagnosis of diseases and development of alternative treatments. Classification and feature selection are frequently used in the statistical analysis of metabolomics data for the detection and discovery of biomarkers. Recently, automatic programming methods have begun to be used instead of conventional methods. In this paper, three conventional classification and feature selection methods (PLS-DA, RF, SVM) and two automatic programming methods (ABCP and GP) are applied to classification problems where they are evaluated on synthetic and real data sets. The selection performances on the biomarker discovery of the algorithms have been compared. It has been found that automatic programming methods are more successful in classifying metabolic data and ABCP is superior to GP in biomarker discovery.

A quick semantic artificial bee colony programming (qsABCP) for symbolic regression

Artificial bee colony programming (ABCP) is a novel evolutionary computation based automatic programming method, which uses the basic structure of artificial bee colony (ABC) algorithm. In this paper, some studies were conducted to improve the performance of ABCP and three new versions of ABCP are introduced. One of these improvements is related to the convergence performance of ABCP. In order to increase the local search ability and achieve higher quality solutions in early cycles, quick ABCP algorithm was developed. Experimental studies validate the enhancement of the convergence performance when the quick ABC approach is used in ABCP. The second improvement introduced in this paper is about providing high locality. Using semantic similarity based operators in the information sharing mechanism of ABCP, semantic ABCP was developed and experiment results show that semantic based information sharing improves solution quality. Finally, combining these two methods, quick semantic ABCP is introduced. Performance of these novel methods was compared with some well known automatic programming algorithms on literature test problems. Additionally, ABCP based methods were used to find approximations of the Colebrook equation for flow friction. Simulation results show that, the proposed methods can be used to solve symbolic regression problems effectively.

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

A Comparative Study of Automatic Programming Techniques

Automatic programming, an evolutionary computing technique, forms the programs automatically and is based on higher level features that can be easily specified than normal programming languages. Genetic Programming (GP) is the first and best-known automatic programming technique that is applied to solve many practical problems. Artificial Bee Colony Programming (ABCP) is one of the latest proposed automatic programming method that combines evolutionary approach with swarm intelligence. GP is an extension version of Genetic Algorithm (GA) and ABCP is based on Artificial Bee Colony (ABC) algorithm. The main differences of these automatic programing techniques and their conventional algorithms (GA and ABC) are modeling solution. In ABC same as GA, the solutions are represented fixed code blocks. In GP and ABCP, the positions of food sources are expressed in tree structure that is composed of different combinations of terminals and functions that are specifically defined as problems. This paper presents a review on GP and ABCP and they are worked in symbolic regression, prediction and feature selection problems which are widely tackled by researchers. The results of the ABCP compared with results of GP show that this algorithm is a powerful optimization technique for structural design.

Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods.

Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete

The use of recycled concrete aggregate to produce new concrete can assist the sustainability in construction industry. However, the mechanical properties of this type of aggregate should be precisely investigated before its using in different applications. The elastic modulus of concrete is one of the most important design parameters in many construction applications. Because of various mix designs, the existing formulas for the elastic modulus of concrete cannot be used for recycled aggregate concrete (RAC). In recent years, there have been a few attempts for predicting the elastic modulus of RAC, especially, with various types of artificial intelligence (AI)methods: In this paper, three automatic regression methods, namely, genetic programming (GP), artificial bee colony programming (ABCP) and biogeography-based programming (BBP) were used for estimating the elastic modulus of RAC. Performances of the different automatic regression models were compared with each other. Moreover, the sensitivity analysis was performed to assess the trend of the elastic modulus as a function of effective input parameters used for developing the different automatic regression models. Overall, the results show that GP, ABCP, and BBP can be used as reliable algorithms for prediction of the elastic modulus of RAC. In addition, the water absorption of the mixed coarse aggregate and the ratio of the fine aggregate to the total aggregate were found as two of the most effective parameters affecting the elastic modulus of RAC.

Biogeography Inspired Optimization Approaches for Optimal Sizing of PSS to Mitigate Electromechanical Mode Oscillations in Power System

This paper presents an application of biogeography inspired optimization methods to enhance the rotor angle stability by mitigating the electromechanical mode oscillations in the presence of Power System Stabilizer (PSS) with a proposed multi objective mathematical model of minimizing damping factor and maximizing the damping ratio in anelectric power system. Newly proposed meta-heuristic optimization techniques named as Biogeography Based Optimization (BBO) and Oppositional Biogeography Based Optimization (OBBO) are utilized to explore the optimal sizing of PSS parameters such as gain, lag-lead time constants and washout time constant. These advances are tested in two area four machine (kundur system) and typical New York-New England (NYNE) (16-machine, 68 buses) interconnected system at different scenarios to show the performance and effectiveness of the proposed methods. Results obtained for both eigenvalue analysis and nonlinear time domain transient analysis are reassuring the validity of the proposed methods for the effective mitigation of electromechanical mode oscillations compared with Genetic Algorithm (GA), Artificial Bee Colony (ABC), BBO and Sequential Quadratic Programming based ABC (ABC-SQP) approaches.

Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques

Abstract This paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size, and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08's equations exhibit a reasonable performance but Persson and Felekoglu et al.'s equations are highly inaccurate for the prediction of SCC elastic modulus.

An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.