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In the industrial Internet environment, concerning about poor cooperation efficiency and high production cost of intercell scheduling, this paper solved the problem in the case of overlapping placement of machines. Firstly, the paper used the hierarchical network design idea to construct a two-layer directed processing network with machines and manufacturing cells as nodes. And it proposed a multi-objective scheduling model aiming at minimizing the average degree, completion time and processing cost by analyzing the relationship among the global cooperation efficiency, redundant processing paths and the first-or der degree. Secondly, according to the strong local search ability of sparrow search algorithm, this paper designed a fusion strategy that based on nondominated sorting genetic algorithm and sparrow search algorithm, and an initial solution generation mechanism basing on clustering coefficient. Finally, the experiment illustrates the correlation between network characteristics and intercell scheduling objectives, so the proposed model and algorithm have higher quality of solution. [ABSTRACT FROM AUTHOR]

Internet is one of the important network carriers of the industrial Internet of Things. Because of its dynamic changes, the path will expire when supporting the transfer of group control commands, which cannot achieves collaborative manufacturing to equipments scattered in different regions. This paper transformed the group control command transfer problem into constrained dynamic multi-Steiner trees problem firstly, and proposed a kind of improved dynamic genetic algorithm. The algorithm proposed a hybrid search strategy with static and dynamic dimensions. It executed static search strategy when network state didn' t change, static search strategy mainly considered alleviating premature phenomena. It executed dynamic search strategy when network state changed, dynamic search strategy mainly considered improving convergence and using static search strategy to alleviate premature phenomena. This paper verified the validity, convergence and global search ability of the algorithm from transfer success rate, convergence delay rate. [ABSTRACT FROM AUTHOR]

In order to solve the problem that symbiotic organisms search algorithm converge slowly and easy to fall into the local optimum, combining quantum genetic algorithm theory, this paper proposed a real-coded quantum symbiotic organisms search algorithm( RQSOS). First, this paper presented the concept of the difference degree based on the principle of triangular fuzzy number, and constructed a variable component vector and a pair of probability amplitude of a allele in a chromosome that could carry more information and enhance the diversity of the solutions. Then it proposed the mode of rotary learning based on the Archimedes spiral, which strengthened the exploration ability of the solution space. Finally it updated the difference degree based on SOS, and carried out the population learning and mutation operations based on the value of the difference degree which could make the whole population evolution rapidly towards the optimal direction and reduced the probability of falling into local optimum. It was verified by numerical optimization and cloud task scheduling problem, and the simulation results show that the RQSOS algorithm can significantly improve the convergence speed and optimization ability, which is a feasible and effective algorithm. [ABSTRACT FROM AUTHOR]

According to the slow convergence speed and low searching precision of traditional backtracking search optimization algorithm, this paper proposed an improved algorithm based on cellular automaton and orthogonal experimental design. Firstly, the algorithm intrcxluced orthogonal experimental design method into the crossover operator to obtain representative high-quality offspring individuals. Then based on the neighbor mcxlel of cellular automaton, it carried out the orthogonal crossover operation of multiple parents in the domain for individuals, which was beneficial to improve the mining capacity and search efficiency of the algorithm. Finally, in order to balance the global searching and local searching ability of the algorithm, it introduced the dynamic proportional weight of excellent individuals into the cross-population to select and update them, with adopted a new dynamic variation equation. The simulation experiments selected 12 standard test functions and compared with 6 other well-behaved algorithms, the results show that the improved algorithm has obvious advantages in convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]

In virtual resources scheduling of cloud manufacturing, algorithms were often inefficient; relational constraints between tasks, as well as logistics time and cost between tasks and sub-tasks, were not fully considered while modeling. In order to solve these problems, this paper constructed a multi-objective virtual resources scheduling model with the goal of minimizing the delivery time, minimizing the service cost and optimizing the quality of service. It used a double-stranded coding method based on project stage, and proposed an adaptive crossover and mutation probability formula, so as to avoid the low efficiency or premature convergence of algorithm caused by the invariance of crossover and mutation probability. On this basis, to guarantee the global and local search algorithm performance, it used an improved genetic algorithm combined with multiple crossover and mutation strategies based on project phase. The example shows that this model is more applicable than the traditional ones, and the improved genetic algorithm has considerable improvement in efficiency, accuracy and stability. [ABSTRACT FROM AUTHOR]