Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization

Abstract

Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

“…Brownian motion (BM) is a stochastic process in which step length is drawn from the probability function defined by a normal distribution with zero mean (μ = 0) and unit variance (σ 2 = 1) [61,62]. The probability density function at point x for BM is calculated via:…”

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

“…All these versions were tested on 28 datasets of UCI repository 1D SMA models (SMAs) [ 67 ] Sonia Marfia et al 2021 This paper elevates the SMA 1D models to elucidate response of SMAs in thermo mechanical models Slime mould algorithm (SMA) [ 68 ] Davut Izci et al 2021 Tested on several benchmark functions. Using PID controllers, the capability of SMA optimization is enhanced Hybrid improved slime mould algorithm with adaptive β hill climbing (BTβSMA) [ 4 ] Kangjian Sun et al 2021 Tested on 16 benchmark functions and is suggested to lighten the unfledged global and local hunt in standard SMA Archerfish hunting optimizer (AHO) [ 69 ] Farouq Zitouni et al 2021 Tested on 10 benchmark functions, 5 engineering problems. AHO replicates the behavior of Archerfish like jumping and shooting to find closer optimum values WLSSA [ 70 ] Hao Ren et al 2021 Tested on 23 benchmark functions.…”

“…Earlier to this article, a few researchers introduced the same algorithm; however, the mode of idea of the algorithm and handling outline is quite diverse from the algorithms suggested in this article. A hybrid optimizer adaptive β hill climbing was combined with slime mould algorithm, here slime mould algorithm is in addition strengthened with Brownian motion and tournament selection to improve exploration capabilities thus producing better quality outcomes in the exploitation phase [ 4 ]. Zheng-Ming Gao et al [ 5 ] introduced a technique named grey wolf optimizer–slime mould algorithm (GWO-SMA) to minimize the influence of uncertainty as low as probable which is best suited for a few benchmark functions and not suggested for engineering design issues.…”

Mutational Slime Mould Algorithm for Gene Selection

“…Other examples include biogeography-based learning particle swarm optimization (BLPSO) [ 37 ], comprehensive learning particle swarm optimizer (CLPSO) [ 38 ], improved grey wolf optimization (IGWO)[ 39 ], and binary whale optimization algorithm (BWOA) [ 40 ], etc. Therefore, SMA has been applied in engineering design problems [ 35 , 41 ], solar photovoltaic cell parameter estimation [ 42 , 43 ], multi-spectral image segmentation [ 44 ], numerical optimization [ 45 ], prediction problems [ 46 , 47 ], support vector regression parameter adjustment [ 48 ] and other aspects. This algorithm is a sufficiently effective meta-heuristic optimization algorithm, but it may have the shortcoming of local optimal convergence and slow convergence speed when dealing with some complex problems.…”