Multi-objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

“…As it can be seen in Table 10 the quality of the solutions between the algorithms is similar, showing that the reduction in the number of evaluations 1.1E41 9.2E51 0.0E+00 f 3 1.2E26 2.2E37 1.7E-08 f 4 1.9E02 3.2E71 4.6E-09 f 5 5.6E01 4.0E01 3.9E01 f 6 1.1E+02 1.2E+02 0.0E+00 f 7 1.1E03 7.8E04 6.5E04 f 8 8.9E+03 8.6E+03 0.0E+00 f 9 1.9E01 2.0E01 0.0E+00 f 1 0 7.9E06 4.2E07 0.0E+00 of fitness function in the PMIM, does not affect the final quality of the solutions.…”

Parametric control to quasi-linear systems based on dynamic compensator and multi-objective optimization

“…Now, we derive the parametric solutions of output feedback matrix K(θ, x) in (28) or (29). Considering Equations (19) and (30), W c (θ, x) can be written as…”

“…Zhou et al transform multi-task multi-view problem into a multi-objective optimization problem and present a cooperative multi-objective quantum-behaved particle swarm optimization algorithm, which is better than other machine-learning algorithms, to solve the multi-objective optimization problem [32]. For more see [29,33], the most notable fact is that variables to be optimized in the above methods possess physical meanings such that it can be only optimized in a given region, that is, local optimal solution. However, in this paper, these variables to be optimized are arbitrary parameters provided by the proposed parametric approach, which have no physical meanings, therefore, the optimized interval is greatly expanded such that it is a benefit to find a globally optimal solution.…”

“…Based on Equation(28) or(29), K(θ, q) can be obtained asK(θ, q) 0.0886 −Θ 4 − 0.0053 −Θ 2 − 0.0241 −Θ 7 + 0.0182 −Θ 8 − 0.1136 −Θ 6 − 00.0886 −Θ 7 + 0.0182 Θ 4 − 0.0053 −Θ 8 − 0.1136 −Θ 2 − 0.0241 −Θ 6 − 0.7821 −Θ 1 − 0.6978 −Θ5 + 0(θ, q) = 0.0351 −0.0875 −0.4002 −0.2083 −0.0632 0.1292 0.2024 −0.3093 , F (θ, q) = −0.0970 0.0453 0.0273 −0.2371 , (70) under the controller (69) or (70), the closed-loop system can be obtained as qξ = 0886 −0.0053 −0.6978 −0.0241 0.0301 0.0027 0.0182 −0.1136 0.0184 −0.7821 −0.0108 0.0395 0.0371 −0.0875 −0.2083 −0.4002 −0.0970 0.0453 −0.0632 0.1292 −0.3093 0Variation diagram of roll angle α.…”

Anode effect prediction based on collaborative two-dimensional forecast model in aluminum electrolysis production

“…These cells have two electrodes: the anode (positive pole) in its upper part; and the cathode (positive pole) on the bottom. They contain mainly carbonaceous materials [14]. The electrolytic voltage is approximately 4 V. A current of approximately 400 kA is conducted through these electrodes, whereby the electrolytic bath is heated to a temperature of approximately 950 o C [15].…”