Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

Wang, Hui; Wang, Wenjun; Xiao, Songyi; Cui, Zhihua; Xu, Minyang; Zhou, Xinyu

doi:10.1016/j.ins.2020.03.064

Cited by 119 publications

(51 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is similar to many heuristic algorithms and belongs to an intelligent optimization algorithm. Good results have been achieved in solving continuous combinatorial optimization problems [30].…”

Section: Bihabca For Minimizing Energymentioning

confidence: 99%

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

Zhou

2021

Journal of Sensors

View full text Add to dashboard Cite

The growing application of body area networks (BANs) in different fields makes the low energy clustering a paramount issue. A clustering optimization algorithm in BANs is a fundamental scheme to guarantee that the essential collected data can be forwarded in a reliable path and improve the lifetime of BANs. Low energy clustering is a technique, which provides a method that shows how to reduce network communication costs in BANs. A careful low energy clustering scheme is one of the most critical means in the research of BANs, which has attracted considerable attention, comprising monitoring capability constraints. However, the classical clustering method leads to high cost when constraints such as large overall energy consumption are undertaken. Hence, a binary immune hybrid artificial bee colony algorithm (BIHABCA), a randomized swarm intelligent scheme applied in BANs, motivated by immune theory and hybrid scheme is introduced. Furthermore, we designed the formulation that considers both distances between two nodes and the length of bits. Finally, we have compared the energy cost optimized by BIHABCA with a shuffled frog leaping algorithm, ant colony optimization, and simulated annealing in the simulation with different quantity of nodes in terms of energy cost. Results show that the energy cost of the network optimized by the proposed BIHABCA method decreased compared to those by the other three methods which mean that the proposed BIHABCA finds the global optima and reduces the energy cost of transmitting and receiving data in BANs.

show abstract

Section: Bihabca For Minimizing Energymentioning

confidence: 99%

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

Zhou

2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Swarm intelligence optimization algorithms (SIOAs) are a series of optimization algorithms that simulate some intelligent characteristics of natural biological population 1 . Many studies reported that SIOAs could effectively solve various optimization problems 2‐8 . There are several popular SIOAs, such as particle swarm optimization, 9 artificial bee colony, 10 firefly algorithm (FA), 11 and cuckoo search 12 .…”

Section: Introductionmentioning

confidence: 99%

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

Self Cite

View full text Add to dashboard Cite

Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.

show abstract

“…Intelligent optimization algorithms (IOAs) are effective tools for dealing with complex optimization problems. [1][2][3][4][5][6] There are several representative IOAs including particle swarm optimization, 7 artificial bee colony, 8 firefly algorithm (FA), 9 bat algorithm, 10 and cuckoo search. 11 FA was originally proposed by Yang, 8 and its idea stems from the flashing attraction behaviors of fireflies.…”

Section: Introductionmentioning

confidence: 99%

“…There are several representative IOAs including particle swarm optimization, 7 artificial bee colony, 8 firefly algorithm (FA), 9 bat algorithm, 10 and cuckoo search 11 . FA was originally proposed by Yang, 8 and its idea stems from the flashing attraction behaviors of fireflies. Like other IOAs, FA has been successfully applied to many research fields 12‐14 …”

Section: Introductionmentioning

confidence: 99%

A modified firefly algorithm based on neighborhood search

2020

Concurrency and Computation

View full text Add to dashboard Cite

Firefly algorithm (FA) is a new member of intelligent optimization algorithms. Several studies demonstrated that FA could successfully deal with several engineering and theoretical optimization problems. Nevertheless, there are some weak points in FA, such as parameter dependence and high computational complexity. To overcome the above issues, this paper proposes a modified FA based on neighborhood search (MFANS). In MFANS, there are three modifications. First, a modified attraction strategy is employed to reduce the complexity. Then, a neighborhood search method is used to search around the best neighborhood solutions. Third, the step parameter is dynamically adjusted. Performance of MFANS is compared with two improved FAs. Results show the effectiveness of MFANS.

show abstract

Improving artificial Bee colony algorithm using a new neighborhood selection mechanism

Cited by 119 publications

References 45 publications

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

A modified firefly algorithm based on neighborhood search

Contact Info

Product

Resources

About