Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

Tsai, Hsing‐Chih

doi:10.1016/j.ins.2013.09.015

Cited by 59 publications

(24 citation statements)

References 34 publications

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“…proposed a meta-heuristic bee colony algorithm to solve the maximum-weight problem [11,12]. Tsai et al [13] introduced an algorithm that imitated honeybees using the method of neighborhood search and random search for combinatorial optimization and function optimization. Karaboga et al [14] successfully applied the colony algorithm to the problem of function extremum optimization and systematically introduced the artificial bee colony (ABC) model.…”

Section: Quantum Artificial Bee Colony Optimization Algorithmmentioning

confidence: 99%

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

Zhang

2017

Polish Maritime Research

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Section: Quantum Artificial Bee Colony Optimization Algorithmmentioning

confidence: 99%

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

Zhang

2017

Polish Maritime Research

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“…Thus, EA is introduced to solve this deterministic equivalent problem. GA [22] is one of the most widely used EAs in recent decades to solve constrained optimization problems [42]. Because of the strong search capability, controllable search process and easy improvability, many scholars [37,39] use GA to solve the CSPP.…”

Section: Algorithm Designmentioning

confidence: 99%

Model and algorithm for 4PLRP with uncertain delivery time

Huang

Ren

Lee

et al. 2016

Information Sciences

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a b s t r a c tTo address the challenge of logistics routing decision under uncertain environment, this paper studies a fourth party logistics routing problem (4PLRP) with uncertain delivery time (4PLRPU). A novel 4PLRPU model based on uncertainty theory is proposed by describing the delivery time of a third party logistics (3PL) provider as an uncertain variable. After that, the model is transformed into an equivalent deterministic model, and several improved genetic algorithms are designed to get solutions. To handle the problem of infeasible solutions in the proposed 4PLRPU, an improved node-based genetic algorithm (INGA) and an improved distance-based genetic algorithm (IDGA) are developed to reduce the computing time required to repair infeasible solutions, and an improved genetic algorithm based on the simple graph and Dijkstra algorithm (SDGA) is proposed to avoid the generation of infeasible solutions. Numerical experiments are conducted to investigate the performance of the proposed algorithms and verify the effectiveness of the proposed 4PLRPU model. The results show that INGA and SDGA are more effective than the standard genetic algorithm and IDGA at solving large-scale problems. Additionally, compared with the expected value model, the 4PLRPU model is more robust.

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“…Then it randomly searches and generates a feasible solution as a new food and conveys the relevant information to the employed bee. Through the collaboration of the above-mentioned three kinds of bees, ABC algorithm gradually converges and obtains the optimal solution or approximate optimal solution in the feasible solution space [10].…”

Section: Artificial Bee Colony Algorithm 31 the Principleof Artificimentioning

confidence: 99%

Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm

Meng

2014

TELKOMNIKA

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Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

Cited by 59 publications

References 34 publications

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

Model and algorithm for 4PLRP with uncertain delivery time

Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm

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