A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems

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

doi:10.1016/j.engappai.2022.105311

Cited by 85 publications

(25 citation statements)

References 174 publications

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“…t i P denotes the best fitness value of the current iteration of position variable, while best G signifies the global optimal fitness value. 5 r are random parameters that are uniformly distributed between 0 and 1.…”

Section: Self-adaptive Weightmentioning

confidence: 99%

“…In this section, we present another part of the Improved Algorithm , which can optimize the search space and speed up the effectiveness of the search process in the IWSCA. The modified search Eq (5), introduced in the IWSCA is expressed as follows:…”

Section: Dynamic Social Strategymentioning

confidence: 99%

“…In the contemporary epoch, meta-heuristic algorithms (MA) [1-3]have garnered significant attention across a broad spectrum of application domains. Examples of wellknown MA algorithms include Particle Swarm Optimization (PSO) (Eberhart & Kennedy, 1995) [4], and Artificial Bee Colony Algorithm (ABC) (Karaboga, 2005) [5]. Additionally, the evolutionary computation and swarm intelligence literature features other algorithms such as Marine Predators Algorithm (MPA) (Faramarzi & Heidarinejad, 2020) [6], Whale Optimization Algorithm (WOA) (Mirjalili & Lewis, 2016) [7], Grey Wolf Optimizer (GWO) (Mirjalili, 2014) [8].…”

Section: Introductionmentioning

confidence: 99%

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Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

chun

2023

Preprint

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The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy Sine Cosine Algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

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Section: Self-adaptive Weightmentioning

confidence: 99%

Section: Dynamic Social Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

chun

2023

Preprint

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“…If the quality of the newly found honey source is higher than the original one, it will abandon the original one and move to the new one for the next search and update. After repeated iterative calculations, it will continuously optimize the gene sequence, Get the optimal result [12].…”

Section: Screening Strategymentioning

confidence: 99%

Research on process route optimization method based on improved hybrid genetic algorithm

Wang,

Liu,

Cheng

et al. 2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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“…Since its invention by Karaboga [24], the ABC algorithm has received increasing attention owing to its flexibility, simplicity of employment, and small number of control parameters [27]. Compared to other evolutionary algorithms, the ABC algorithm can escape local optima [28], [29] in several real-world problems [30]- [32] and is widely used in the field of combinatorial optimization problems [33] such as traveling salesman [34], vehicle routing [35], [36], graph coloring [37], team orienteering [38],…”

Section: Introductionmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

2023

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The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several stateof-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.INDEX TERMS Artificial bee colony algorithm, multiple-choice multidimensional knapsack problem, hamming distance, surrogate relaxation.

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A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems

Cited by 85 publications

References 174 publications

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

Improved sine cosine algorithm for optimization problems based on self-adaptive weight and social strategy

Research on process route optimization method based on improved hybrid genetic algorithm

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

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