Using the Bees Algorithm to Assign Terminals to Concentrators

Bernardino, Eugénia Moreira; Bernardino, Anabela Moreira; Sánchez‐Pérez, Juan M.; Gómez–Pulido, Juan A.; Vega‐Rodríguez, Miguel A.

doi:10.1007/978-3-642-13025-0_29

Cited by 5 publications

(11 citation statements)

References 3 publications

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“…Bernardino et al [19] proposed a Hybrid ACO (HACO) algorithm to solve TAP and compared the results with the traditional methods. Bernardino et al [20] proposed a BA to solve TAP and compared the results with LSGA, TS, HDE and HACO.…”

Section: Previous Workmentioning

confidence: 99%

“…In this paper, we compare the proposed algorithms with the algorithms proposed by Bernardino et al [8,9,[11][12][13][16][17][18][19][20], because they: (1) use the same 9 small test instances; (2) adopt the same fitness function; (3) implement the algorithms using the same language (C++), and; (4) adopt the same representation (terminalbased).…”

Section: Previous Workmentioning

confidence: 99%

“…The parameters w1 and w2 (w1+w2=1) are used to control the importance of each term (balance/ distances) in the fitness function. In this paper, parameters w1 and w2 are set to 0.9 and 0.1, respectively, which is the same used in [4,8,9,[11][12][13][16][17][18][19][20]. The fitness function penalises also infeasible solutions (6).…”

Section: X(t)-concentrator Assigned To Terminal Tmentioning

confidence: 99%

“…Several heuristics have been used to optimise TAP: Local Search (LS) methods [6][7][8][9], EAs [3][4][5][9][10][11][12][13][14][15][16][17][18], SI techniques [19,20], among others. Note however, that this paper presents the first attempt (to the authors' knowledge) to use evolutionary swarm based algorithms to optimise TAP.…”

Section: Introductionmentioning

confidence: 99%

“…We compare the performance of GAS and QBEA with: Genetic Algorithm (GA), Tabu Search (TS) [9], Hybrid GA (HGA) [9], Local Search GA (LSGA) [11], GA with Multiple Operators (GAMO) [12], Hybrid Differential Evolution (HDE) [13], BA [20], Hybrid ACO (HACO) [19], Hybrid Scatter Search (HSS) [16], Discrete Differential Evolution (DDE) [17] and Hybrid Population Based Incremental Learning (HPBIL) [18].…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Swarm based algorithms to minimise the link cost in Communication Networks

Bernardino¹,

Bernardino²,

Sánchez‐Pérez³

et al. 2012

IJCIS

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In the last decades, nature-inspired algorithms have been widely used to solve complex combinatorial optimisation problems. Among them, Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) algorithms have been extensively employed as search and optimisation tools in various problem domains. Evolutionary and Swarm Intelligent algorithms are Artificial Intelligence (AI) techniques, inspired by natural evolution and adaptation. This paper presents two new nature-inspired algorithms, which use concepts of EAs and SI. The combination of EAs and SI algorithms can unify the fast speed of EAs to find global solutions and the good precision of SI algorithms to find good solutions using the feedback information. The proposed algorithms are applied to a complex NP-hard optimisation problem -the Terminal Assignment Problem (TAP). The objective is to minimise the link cost to form a network. The proposed algorithms are compared with several EAs and SI algorithms from literature. We show that the proposed algorithms are suitable for solving very large scaled problems in short computational times.

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Section: Previous Workmentioning

confidence: 99%