Dynamic Simulated Annealing with Adaptive Neighborhood Using Hidden Markov Model

Limam, Mohamed; Afia, Abdellatif El; Chiheb, Raddouane

doi:10.1007/978-3-030-58930-1_11

Cited by 1 publication

(1 citation statement)

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“…To reduce the high time complexity of capacitated vehicle routing issues, an enhanced simulated annealing algorithm combined with crossover operator (ISA-CO) was suggested in [ 126 ] to improve convergence. Using hidden Markov model (HHM), dynamic simulated annealing was introduced in [ 127 ], with the integration of HHM adapts neighborhood structure at every iteration in SA, thus proving the capability optimum nature of fellow function depending on the history of search. On the whole, in every observation of an algorithm, it is noted that many cases experience precipitate convergence in simulation results.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Kumari

Kamboj

Bath

2022

Complex Intell. Syst.

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The existing slime mould algorithm clones the uniqueness of the phase of oscillation of slime mould conduct and exhibits slow convergence in local search space due to poor exploitation phase. This research work exhibits to discover the best solution for objective function by commingling slime mould algorithm and simulated annealing algorithm for better variation of parameters and named as hybridized slime mould algorithm–simulated annealing algorithm. The simulated annealing algorithm improves and accelerates the effectiveness of slime mould technique as well as assists to take off from the local optimum. To corroborate the worth and usefulness of the introduced strategy, nonconvex, nonlinear, and typical engineering design difficulties were analyzed for standard benchmarks and interdisciplinary engineering design concerns. The proposed technique version is used to evaluate six, five, five unimodal, multimodal and fixed-dimension benchmark functions, respectively, also including 11 kinds of interdisciplinary engineering design difficulties. The technique’s outcomes were compared to the results of other on-hand optimization methods, and the experimental results show that the suggested approach outperforms the other optimization techniques.

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Section: Literature Reviewmentioning

confidence: 99%