Solving the Terminal Assignment Problem Using a Local Search Genetic Algorithm

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

“…Bernardino et al [12] proposed a GA with multiple operators (GAMO) for crossover and mutation and compared it with the traditional methods. Bernardino et al [11] proposed a Local Search GA (LSGA) and compared the LSGA with TS, HGA and GAMO. Julstrom [15] proposed three permutation-coded GAs and compared the results between them.…”

“…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).…”

“…In [11], Bernardino et al proposed a Local Search GA (LSGA). It combines global and LS by using a GA to perform exploration, while the LS method performs exploitation.…”

“…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.…”

“…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].…”