A survey on the Artificial Bee Colony algorithm variants for binary, integer and mixed integer programming problems

Akay, Bahriye; Karaboğa, Derviş; Görkemli, Beyza; Kaya, Ebubekir

doi:10.1016/j.asoc.2021.107351

Cited by 74 publications

(38 citation statements)

References 115 publications

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“…We will not provide any introduction to ABC algorithm as it is a well-established metaheuristic technique. However, those requiring such an introduction may refer to this recent survey (Akay et al, 2021).…”

Section: The Abc Approach To Mwdds Problemmentioning

confidence: 99%

Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

Nakkala

Singh

Rossi

2022

Engineering Applications of Artificial Intelligence

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Section: The Abc Approach To Mwdds Problemmentioning

confidence: 99%

Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

Nakkala

Singh

Rossi

2022

Engineering Applications of Artificial Intelligence

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“…In addition, the two offload decision variables are binary arrays, so the offload decision is a nonlinear restricted 0–1 planning problem, while the communication and computational resource allocation problems have a large number of variables with linear relationships between the user tasks, and the literature [ 25 ] has shown that the underlying problem for the joint optimization of computational offload, communication, and computational resource allocation is a mixed integer nonlinear programming (MINP) problem, which is difficult to find the optimal solution, so the complex problem is decomposed into a low-complexity problem to solve.…”

Section: Model Construction and Problem Transformationmentioning

confidence: 99%

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Han

Huang

et al. 2022

Computational Intelligence and Neuroscience

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To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the “cloud, fog, edge, and end” collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.

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“…In contrast, EAs can be used for almost all of the problems without difficulty, which may be one of the most attractive advantages of EAs 2–4 . Being inspired by the biological evolution phenomenon, that is, the survival of the fittest, EAs are comprised of various paradigms with different metaphors, 5,6 such as the genetic algorithm (GA), 7 particle swarm optimization algorithm (PSO), 8 differential evolution algorithm (DE), 9 and artificial bee colony algorithm (ABC) 10 . For instance, the GA simulates the mutation and crossover behavior of biological genes, 11 and the PSO imitates the flying and preying behavior of bird flocks 12 .…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

Zhou

Huang

Tang

et al. 2022

Int J of Intelligent Sys

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As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi‐coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real‐world optimization problem, and four well‐established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

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A survey on the Artificial Bee Colony algorithm variants for binary, integer and mixed integer programming problems

Cited by 74 publications

References 115 publications

Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

Swarm intelligence, exact and matheuristic approaches for minimum weight directed dominating set problem

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

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