A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior

Wedde, Horst F.; Lehnhoff, Sebastian; Bonn, Bernhard van; Bay, Z.; Becker, Stefan; Böttcher, Sven; Brunner, C.; Büscher, Armin; Fürst, Thomas; Lazarescu, Anca M.; Rotaru, Elisei; Senge, Sebastian; Steinbach, Bastian; Yılmaz, Fatih; Zimmermann, Thomas

doi:10.1109/efta.2007.4416912

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…Wedde et al [234] used a bee algorithm (e.g., BeeHive algorithm development) to route vehicles on the roads with lower traffic jams and congestions to minimize transportation times. The topological road data of eastern German Ruhr District was considered and several traffic scenarios were generated.…”

Section: Discrete Applicationsmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

471

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: Discrete Applicationsmentioning

confidence: 99%

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

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

471

213

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“…Few algorithms inspired by bees' behavior have appeared during the last decade, for example the Bee Colony Optimization (BCO) algorithm (Lučić and Teodorović 2001, 2003a, Marriage Bee Optimization (MBO) (Abbass 2001), BeeHive (Wedde et al 2004(Wedde et al , 2007, Artificial Bee Colony (ABC) algorithm (Karaboga 2005;Karaboga et al 2007), Bees Swarm Optimization (BSO) (Drias et al 2005), Virtual Bee Algorithm (VBA) (Yang 2005), Bees Algorithm (Pham et al 2006), Honey Bee Colony Algorithm (Chong et al 2006), Bee Hive Model (Navrat 2006), Honey Bee Social Foraging Algorithm Passino 2007a, 2007b), and Honey Bee Mating Optimization (HBMO) (Afshar et al 2007). Although based on different principles, all these algorithms may represent useful tools for dealing with different combinatorial optimization problems.…”

Section: Bee Colony Optimizationmentioning

confidence: 99%

Bee colony optimization for scheduling independent tasks to identical processors

et al. 2012

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The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.T. Davidović ( ) Mathematical Institute, Serbian Academy of Sciences and Arts,

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“…Wang et al (2007) proposed a QoS unicast routing scheme with always best connected supported based on beehive algorithm. Wedde et al (2007) presented a completely decentralized multi-agent approach (termed BeeJamA) on multiple layers where car or truck routing are handled through algorithms adapted from the BeeHive algorithms which in turn have been derived from honey bee behavior. They reported superior performance of BeeJamA over conventional approaches (Wedde et al 2008).…”

Section: Queen Beementioning

confidence: 99%

A survey: algorithms simulating bee swarm intelligence

2009

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Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

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A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior

Cited by 18 publications

References 10 publications

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

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

Bee colony optimization for scheduling independent tasks to identical processors

A survey: algorithms simulating bee swarm intelligence

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