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Soybean protein was taken as a model protein to investigate two aspects of the protein extraction by sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelles: (1) the forward protein extraction from the solid state, and the effect of pH, AOT concentration, alcohol and water content (W0) on the transfer efficiency; (2) the back-transfer, the capability of the protein to be recovered from the micellar solution. The experimental results led to the conclusion that the highest forward extraction efficiency of soybean protein was reached at AOT concentration 180 mmol l(-1), aqueous pH 7.0, KCl concentration 0.05 mol l(-1), 0.5 % (v/v) alcohol, W0 18. Under these conditions, the forward extraction efficiency of soybean protein achieved 70.1 %. It was noted that the percentage of protein back extraction depended on the salt concentration and pH value. Around 92 % of protein recovery was obtained after back extraction.

In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.

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