An Artificial Fish Swarm Algorithm for the Multicast Routing Problem

Liu, Qing; Odaka, Tomohiro; Kuroiwa, Jousuke; Shirai, Haruhiko; Ogura, Hisakazu

doi:10.1587/transcom.e97.b.996

Cited by 11 publications

(10 citation statements)

References 22 publications

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“…The AFSA is inspired by the individual, group and social behaviors of the fish. In a social form, it is searching for food, immigration, dealing with dangers and interactions between the fish in a swarm that result in intelligent group behavior [43,44]. The AFSA has many advantages such as flexibility, great convergence speed, great accuracy, fault tolerance and so on [45].…”

Section: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

et al. 2018

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Tuned mass damper (TMD) has a wide application in the human-induced vibration control of pedestrian bridges and its parameters have great influence on the control effects, hence it should be well designed. A new optimization method for a TMD system is proposed in this paper, based on the artificial fish swarm algorithm (AFSA), and the primary structural damping is taken into consideration. The optimization goal is to minimize the maximum dynamic amplification factor of the primary structure under external harmonic excitations. As a result, the optimized TMD has a smaller maximum dynamic amplification factor and better robustness. The optimum TMD parameters for a damped primary structure with different damping ratios and different TMD mass ratios are summarized in a table for simple, practical design, and the fitting equation is also provided. The TMD configuration optimized by the proposed method was shown to be superior to that optimized by other classical optimization methods. Finally, the application of an optimized TMD based on AFSA for a pedestrian bridge is proposed as a case study. The results show that the TMD designed based on AFSA has a smaller maximum dynamic amplification factor than the TMD designed based on the classic Den Hartog method and the TMD designed based on the Ioi Toshihiro method, and the optimized TMD has a good effect in controlling human-induced vibrations at different frequencies.

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Section: Artificial Fish Swarm Algorithmmentioning

confidence: 99%

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

et al. 2018

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“…Intelligence algorithms have recently been developed [4]- [8], and they can create smaller cost Steiner trees at the expense of longer algorithm processing times. Their combinations between the exploitation and exploration processes are different, but the main idea with them is comparing and moving the Steiner positions of individual agents repeatedly until the end of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, multi-agent BBMC is compared with one of the intelligence algorithms, AFSA, which, it is claimed, can approach the minimum Steiner tree more closely compared with other intelligence algorithms, such as PSO and GA [8].…”

Section: Evaluationsmentioning

confidence: 99%

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Multi-Agent Steiner Tree Algorithm Based on Branch-Based Multicast

Matsuura

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThe Steiner tree problem is a nondeterministic-polynomial-time-complete problem, so heuristic polynomial-time algorithms have been proposed for finding multicast trees. However, these polynomialtime algorithms' tree-cost optimality rates are not sufficient to obtain effective multicast trees, so intelligence algorithms, such as the genetic algorithm and artificial fish swarm algorithm, were proposed to improve previously proposed polynomial-time algorithms. However, these intelligence algorithms are time-consuming, even though they can reach quasi-optimal multicast trees. This paper proposes the multi-agent branch-based multicast (BBMC) algorithm, which can maintain the fast speed of polynomialtime algorithms while matching the tree-cost optimality of intelligence algorithms. The advantage of the proposed multi-agent BBMC algorithm is its covering of discarded effective branch candidates to seek the optimal multicast tree. By saving these branch candidates, the algorithm incurs tree-costs that are as small as those of intelligence algorithms, and by saving only a limited number of effective candidates, the algorithm is much faster than intelligence algorithms.

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“…The algorithm has the ability of parallel, high efficiency and strong ability to obtain the global optimal solution [3][4]. Currently, the algorithm has been widely used in combinatorial optimization, parameter estimation and other problems, and has achieved good results on multi class continuous function optimization problems [5][6][7][8][9][10]. However, as for the optimization problems with integer domain, the AFSA need integer constraints to meet the characteristic of the problem.…”

Section: Introductionmentioning

confidence: 99%

An Integer Artificial Fish Swarm Algorithm and Its Application on Ambiguity Resolution

Wang¹,

Wang²,

Honglei³

2017

dtcse

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Abstract. In order to solve the integer optimization problem and improve the search efficiency of GPS ambiguity resolution, in view of the problem characteristics, an integer artificial fish swarm algorithm (IAFSA) is introduced to solve the problem of integer ambiguity resolution. The algorithm added the integer constraints to the classical AFSA and adopted a attenuation factor to adjust the visual field and step length of the artificial fish adaptively. According to the simulation results, compared with the genetic algorithm (GA), the IAFSA can get integer ambiguity solution quickly, and has the higher search efficiency and reliability.

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An Artificial Fish Swarm Algorithm for the Multicast Routing Problem

Cited by 11 publications

References 22 publications

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

Application of an Artificial Fish Swarm Algorithm in an Optimum Tuned Mass Damper Design for a Pedestrian Bridge

Multi-Agent Steiner Tree Algorithm Based on Branch-Based Multicast

An Integer Artificial Fish Swarm Algorithm and Its Application on Ambiguity Resolution

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