Quick Artificial Bee Colony Algorithm Research Articles

On the Application of Quick Artificial Bee Colony Algorithm (qABC) for Attenuation of Test Suite in Real-Time Software Applications

Software testing plays a vital role during the software development process, as it ensures quality software deployment. Success of software testing depends on the design of effective test cases. To achieve the optimization of generated test cases, the proposed approach combines both global and local searches by means of intelligent agents which exhibit the behaviour of employed bees, onlooker bees, and scout bees in the qABC algorithm. The proposed qABC algorithm has key improvements over the basic artificial bee colony algorithm (ABC) in test optimization by reducing redundancy, filtering of test cases in each iteration and parallel working of the bees. Further, the fitness evaluation of the test cases is done by employing two test adequacy metrics namely path coverage and mutation score. Further, the experimental evaluation of qABC, GA, and the basic ABC based test cases is done using several case study applications. The result shows that qABC outperforms the other algorithms in terms of effectiveness of test cases in revealing the faults with less time and a smaller number of test cases.

Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures

Artificial bee colony algorithm (ABC) has been efficiently used to obtain the optimum design of structures under design code limitations. There are several enhancements suggested to improve the performance of ABC even further in the literature. In this chapter, the performance of enhanced ABC variants in the optimum design of steel skeletal frames is studied. The mathematical model of such design problem when based on the provisions of design code requirements results in a discrete nonlinear programming problem. Seven variants of ABC is selected from the literature. These are standard artificial bee colony algorithm (sABC), artificial bee colony algorithm with orthogonal learning (OCABC), quick artificial bee colony algorithm (qABC), noel chaotic artificial bee colony algorithm (STOC-BC), directed artificial bee colony algorithm (dABC), improved artificial bee colony algorithm (NSABC) and artificial bee colony algorithm with Levy flight distribution (ABC_Levy). Seven optimum design algorithms are developed to obtain the solution of the design problem formulated considering design code provisions. Two steel space frames, one ten story and the other fifteen story are optimized using each of the seven optimum design techniques. The performance of each variant is observed and the optimum designs are compared. It is noticed after carrying out the statistical analysis that ABC_Levy and qABC algorithms outperform the other algorithms.

A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

On the performance of quick artificial bee colony algorithm for dynamicdeployment of wireless sensor networks

In recent years, the use of wireless sensor networks (WSNs) has increased and there have been significant improvements in this field. Especially with smarter, cheaper, and smaller sensor nodes, various kinds of information can be detected and collected in different environments and under different conditions. WSNs have thus been used in many applications such as military, surveillance, target tracking, home, medical, and environmental applications. As the popularity of WSNs increases, problems related to these networks are being realized. The dynamic deployment problem is one of the main challenges that have a direct effect on the performance of WSNs. In this study, a novel optimization technique named the quick artificial bee colony (qABC) algorithm was applied to the dynamic deployment problem of WSNs. qABC is a new version of the artificial bee colony algorithm (ABC) and it redefines the onlooker bee phase of ABC in a more detailed way. In order to see the performance of qABC on this problem, WSNs that include only mobile sensors or both stationary and mobile sensors were considered with binary and probabilistic detection models. Some experimental studies were conducted for tuning the colony size ($CS$) and neighborhood radius ($r$) parameters of the qABC algorithm, and the performance of the proposed method was compared with the standard ABC algorithm and some other recently introduced approaches including a parallel ABC, a cooperative parallel ABC, a version of ABC powered by a transition control mechanism (tlABC), and a parallel version of tlABC. Additionally, some CPU time analyses were provided for qABC and ABC considering different dimensions of the problem. Simulation results show that the qABC algorithm is an effective method that can be used for the dynamic deployment problem of WSNs, and it generally improves the convergence performance of the standard ABC on this problem when $r \geq 1$.

An Improved Quick Artificial Bee Colony Algorithm for Portfolio Selection

Computation Intelligence has inspired many researchers to develop the capability of computers to learn and solve a complex task in real-world problems. In this work, we propose an Artificial Bee Colony (ABC) to deal with the Stock Selection problem. We apply a Sigmoid-based Discrete-Continuous model with ABC to select appropriate features for stock scoring. The empirical study tests the performance of ABC compared with Genetic Algorithm (GA) and Differential Evolution (DE) algorithm by using data from the Stock Exchange Thailand. The empirical results show that the novel model stock selection significantly outperforms in terms of both investment return, diversity and model robustness.

A Quick Artificial Bee Colony Algorithm for Image Thresholding

The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur’s entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur’s entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality.

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur’s entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image’s spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur’s entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.