A Comprehensive Survey on Artificial Bee Colony Algorithm as a Frontier in Swarm Intelligence

Agarwal, Shiv Kumar; Yadav, Surendra Kumar

doi:10.1007/978-981-13-5934-7_12

Cited by 13 publications

(5 citation statements)

References 26 publications

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“…The operational steps of all the iterative optimization algorithms consist of three major stages namely the algorithm initialization, updating the solutions and termination of the algorithm [70]. During the initialization stage, the initial solution search space is defined by randomly generating the solution search space by keeping in consideration the dependent and independent variables of the optimization problem and the dependent values of optimization function.…”

Section: A Problem Contextualizationmentioning

confidence: 99%

An Enhanced Firefly Algorithm Using Pattern Search for Solving Optimization Problems

et al. 2020

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Firefly Algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches used for solving optimization problems. The conventional FA use randomization factor during generation of solution search space and fireflies position changing, which results in imbalanced relationship between exploration and exploitation. This imbalanced relationship causes in incapability of FA to find the most optimum values at termination stage. In the proposed model, this issue has been resolved by incorporating PS at the termination stage of standard FA. The optimized values obtained from the FA are set as the initial starting points for the PS algorithm and the values are further optimized by PS to get the most optimal values or at least better values than the values obtained by conventional FA during its maximum number of iterations. The performance of the newly developed FA-PS model has been tested on eight minimization functions and six maximization functions by considering various performance evaluation parameters. The results obtained have been compared with other optimization algorithms namely genetic algorithm (GA), standard FA, artificial bee colony (ABC), ant colony optimization (ACO), differential equations (DE), bat algorithm (BA), grey wolf optimization (GWO), Self-Adaptive Step Firefly Algorithm (SASFA), and FA-Cross algorithm in terms of convergence rate and various numerical performance evaluation parameters. A significant improvement has been observed in the solution quality by embedding PS in the standard FA at the termination stage. The result behind this improvement is the better exploration and exploitation of the solution search space at this stage.

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Section: A Problem Contextualizationmentioning

confidence: 99%

An Enhanced Firefly Algorithm Using Pattern Search for Solving Optimization Problems

et al. 2020

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“…Several algorithms which imitate the behaviour of those intelligent systems have been proposed. This article focuses on some of the most used: Ant-based algorithms [14,15], Particle Swarm Optimisation [16,17], Artificial Swarm Fish Algorithm [18], Artificial Bee Colonies [19,20] and Firefly Algorithm [21,22]. Some recent articles that analyse publications related to swarm methods show that the selected methods are among those considered in more publications [1,7].…”

Section: Swarm Algorithmsmentioning

confidence: 99%

A Proposal for the Organisational Measure in Intelligent Systems

Román

Delgado

2020

Applied Sciences

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The collaboration within organisations and among organisations is a fundamental concept in the attainment of the overall objectives pursued by an enterprise network in human companies. Swarm systems are intelligent systems that show collaboration within the system; moreover, some models, such as multiple ant colonies, show the collaboration of several systems to achieve a global goal. The collaboration in this type of system optimises the achievement of the overall objectives as in an enterprise network in human organisations. Being able to measure this collaboration allows establishing a relationship between the improvement in the results of the system and the degree of collaboration, both at the level of specialisation of each element of the system and the systems as a whole. The performance of a swarm system depends on the number of members in many cases, so that if we can establish a measure of specialisation and collaboration, we could tipify and classify these systems in terms of the efficiency and the realiability to perform different tasks.

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“…Nevertheless, as stated in the literature, same with other algorithms, ABC has a widely-known performance problem which is poor exploitation ability [21]- [23]. Hence, modification is necessary before its application to actual case scenario, specifically if the variables dimension is high like in this case.…”

Section: Introductionmentioning

confidence: 97%

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

Cutad¹,

Gerardo²

2019

IJACSA

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Due to the vast amount of students' information and the need of quick retrieval, establishing databases is one of the top lists of the IT infrastructure in learning institutions. However, most of these institutions do not utilize them for knowledge discovery which can aid in informed decision-making, investigation of teaching and learning outcomes, and development of prediction models in particular. Prediction models have been utilized in almost all areas and improving the accuracy of the model is sought-after this study. Thus, the study presents a Scoutless Rule-driven binary Artificial Bee Colony (SRABC) as a searching strategy to enhance the accuracy of the prediction model for curriculum analysis. Experimental verification revealed that SRABC paired with K-Nearest Neighbor (KNN) increases the prediction accuracy from 94.14% to 97.59% than paired with Support Vector Machine (SVM) and Logistic Regression (LR). SRABC is efficient in selecting 14 out of 60 variables through majority voting scheme using the data of the BSIT students of Davao Del Norte State College (DNSC),

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A Comprehensive Survey on Artificial Bee Colony Algorithm as a Frontier in Swarm Intelligence

Cited by 13 publications

References 26 publications

An Enhanced Firefly Algorithm Using Pattern Search for Solving Optimization Problems

An Enhanced Firefly Algorithm Using Pattern Search for Solving Optimization Problems

A Proposal for the Organisational Measure in Intelligent Systems

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

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