Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Shah, Habib; Herawan, Tutut; Naseem, Rashid; Ghazali, Rozaida

doi:10.1007/978-3-319-11857-4_23

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…In this section, the performance of HFEABC (H) is compared with other famous ABC variants, which involves the standard ABC (A1) [4], AABCLS (A2) [38], BSFABC (A3) [17], GABC (A4) [15], GABCS (A5) [40], GBABC (A6) [20], HGABC (A7) [41], IABC (A8) [36], and LABCSS (A9) [37]. The functions used to test these algorithms are described in Section 5.1.…”

Section: Experiments and Discussionmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

Cai

et al. 2018

Mathematical Problems in Engineering

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The artificial bee colony (ABC) algorithm is a relatively new optimization technique for simulating the honey bee swarms foraging behavior. Due to its simplicity and effectiveness, it has attracted much attention in recent years. However, ABC search equation is good at global search but poor at local search. Some different search equations are developed to tackle this problem, while there is no particular algorithm to substantially attain the best solution for all optimization problems. Therefore, we proposed an improved ABC with a new search equation, which incorporates the global search factor based on the optimization problem dimension and the local search factor based on the factor library (FL). Furthermore, aimed at preventing the algorithm from falling into local optima, dynamic search balance strategy is proposed and applied to replace the scout bee procedure in ABC. Thus, a hybrid, fast, and enhanced algorithm, HFEABC, is presented. In order to verify its effectiveness, some comprehensive tests among HFEABC and ABC and its variants are conducted on 21 basic benchmark functions and 20 complicated functions from CEC 2017. The experimental results show HFEABC offers better compatibility for different problems than ABC and some of its variants. The HFEABC performance is very competitive.

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Section: Experiments and Discussionmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

Cai

et al. 2018

Mathematical Problems in Engineering

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“…In [146] the authors modified the employed bee phase of ABC by using best solutions as well as random numbers in local and global neighbourhoods thus providing balanced exploration and exploitation. S h a h et al [147] presented a hybrid ABC by incorporating the global best solution in solution search equation of employed and onlooker bees phase. The scout bee phase was also modified by including the best-guided strategy.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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“…The original bees search around the current solution for a random neighbor towards to one of the existing solutions. Some articles (Gao and Liu 2012) (Shah et al 2014) allow bees to search around the best solution of the current population. Although such approaches enable bees to converge the optimal solution very soon, they increases possibility of local stuck around the near optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Honey formation optimization: HFO

Yetgin

Şamdan

2021

Turkish Journal of Engineering

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In this paper, a new optimization framework, namely Honey Formation Optimization (HFO), is proposed. In contrary to the Artificial Bee Colony Optimization (ABC) variants in literature, the HFO considers food sources consisting of many components and model the honey formation inside bees as a process of mixing the components with their special enzymes during chewing up the food source. We believe that bees analyze the amounts of components inside the food source and attempt more to collect weaker (less amount) components to improve the honey formation process. Thus, each time a worker exploits a food source it selects a component in such a way that weaker components are more frequently selected. The approach requires decomposing the solution into components where each component is evaluated by a component fitness function. The honey formula maps the component fitness to honey amount and considered as the equivalence of the fitness function. The worker bee uses the fitness of the selected component to evaluate the food source and does local search only around the selected component. The HFO and ABC Frameworks are compared on the basis of 9 benchmark functions. The result shows that HFO performs better than the ABC.

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Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization

Cited by 12 publications

References 15 publications

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Honey formation optimization: HFO

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