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

Abstract

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.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…Hybridization with the Simulated Annealing (SA) Algorithm Izci D et al [30] proposed an opposition-based hybrid SMA with an SA algorithm to improve the exploitation and exploration factors of the original SMA. Leela Kumari Ch et al [94] used an SA algorithm to help the SMA avoid trapping the local optimum. 4.2.6.…”

“…Örnek BN et al [83] tested the performance of the proposed SMA on the designs' optimizations and the results demonstrated the proposed SMA had a better ability to jump out of local optima. Leela Kumari Ch et al [94] presented a hybrid version of an SMA and assessed it in 11 kinds of interdisciplinary engineering designs. In contrast to the typical engineering design applications described above, few SMA variants are applied to perform reliability-based design optimizations (RBDO).…”

Hardware implementation of a novel hybrid MPPT technique for fast tracking of GMPP in solar PV system under PSCs

“…It is a newly framed stochastic optimizer which is developed based on foraging nature of the creature slime mould (Ch et al , 2022). Unlike other colonial bacteria, this one does not have a central neurological system.…”