A survey on bee colony algorithms

Bitam, Salim; Batouche, Mohamed; Talbi, El‐Ghazali

doi:10.1109/ipdpsw.2010.5470701

Cited by 54 publications

(32 citation statements)

References 26 publications

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“…Surveys of the different developments can be found in [103,104,105]. In this paper, we mainly focus on the ABC algorithms that have attracted most of the attention, especially in DOPs [105].…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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“…The domain of bee-inspired algorithms is not constrained to the ones presented in this section, but these are the ones so far used to tackle vehicle routing problems. As shown in Table 1, these algorithms can be divided into two subclasses based on the bees' behavior used as inspiration [23]. Each of the base algorithms is described in the following subsections.…”

Section: Bee-inspired Algorithms: a Taxonomy And Standard Approachesmentioning

confidence: 99%

“…Some of the main collective behaviors of bee colonies during foraging that can be highlighted as inspiration for the design of algorithms are as follows [21]: (1) bees dance to recruit nestmates to a food source; (2) bees adjust the exploration and recovery of food according to the colony state; (3) bees exploit multiple food sources simultaneously; (4) there is a positive linear relationship between the number of bees dancing and the number of bees recruited; (5) recruitment continues until a threshold number of bees is reached; (6) the quality of the food source influences the bee dance; and (7) all bees retire at some point in time, meaning that bees stop recruiting other bees. For generic reviews of bee-inspired algorithms and applications, the reader is invited to refer to Karaboga and Akay [22], Bitam et al [23], Ruiz-Vanoye et al [24], Verma and Kumar [25], Karaboga et al [26], and Agarwal et al [27].…”

Section: Introductionmentioning

confidence: 99%

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

Masutti

Castro

2017

Mathematical Problems in Engineering

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Vehicle routing problems constitute a class of combinatorial optimization tasks that search for optimal routes (e.g., minimal cost routes) for one or more vehicles to attend a set of nodes (e.g., cities or customers). Finding the optimal solution to vehicle routing tasks is an NP-hard problem, meaning that the size of problems that can be solved by exhaustive search is limited. From a practical perspective, this class of problems has a wide and important set of applications, from the distribution of goods to the integrated chip design. Rooted on the use of collective intelligence, swarm-inspired algorithms, more specifically bee-inspired approaches, have been used with good performance to solve such problems. In this context, the present paper provides a broad review on the use of bee-inspired methods for solving vehicle routing problems, introduces a new approach to solve one of the main tasks in this area (the travelling salesman problem), and describes open problems in the field.

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“…In the exploitation step, bee collects food and calculates its quantity to make a new decision. Either, it continues collecting by memorization of this best location or it leaves the food and returns to the beehive as simple bee [11].…”

Section: A Bees Communication Principalementioning

confidence: 99%

QoS Swarm Bee Routing Protocol for Vehicular Ad Hoc Networks

Bitam

Mellouk

2011

2011 IEEE International Conference on Communications (ICC)

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Research and industries are recently more interesting and attracting to the Vehicular Ad hoc Networks (VANETs) development domain. They contribute to safer and more efficient roads by providing timely and accuracy information to drivers and authorities. Thus, the definition of a quality of service routing protocol for VANETs is one of their challenges. In this paper, we propose QoSBeeVanet, a new quality of service multipath routing protocol adapted for the vehicular ad hoc networks. It is based on ideas of the autonomic bee communication. Simulation results taken with NS2 in realist urban settings were shown that QoSBeeVanet outperforms DSDV and AODV two of the current state-of-the-art protocols, in terms of end-to-end delay, packet delivery ratio, and normalized overhead load.

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A survey on bee colony algorithms

Cited by 54 publications

References 26 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Bee‐Inspired Algorithms Applied to Vehicle Routing Problems: A Survey and a Proposal

QoS Swarm Bee Routing Protocol for Vehicular Ad Hoc Networks

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