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In order to solve the disadvantage of the traditional whale optimization algorithm, which is slow convergence and easy to fall into local optimum, this paper proposed a mixed strategy based whale optimization algorithm. Firstly, it introduced the nonlinear adjustment strategy to modify the convergence factor, balanced the exploration and exploitation capability and accelerated the convergence speed. Then, it introduced an adaptive weighted coefficient into the position update formula of whales to improve the search precision of the algorithm. Finally, it combined the limit threshold idea of artificial bee colony algorithm to effectively jump out of the local optimum and prevent premature convergence. The results show that the proposed algorithm has better search precision and convergence speed through experiments on different dimensions of 14 benchmark functions. [ABSTRACT FROM AUTHOR]

The traditional immune network optimization algorithm had the shortcomings of weak local searching ability, low precision and premature convergence. Inorder to improve the algorithm performance, this paper proposed an artificial-immune-network-based memetic algorithm for dynamic optimization problems. Based on the framework of memetic algorithm, an artificial immune network algorithm served as the global search algorithm, and a tabu search algorithm served as the local search operator. At the same time, the algorithm introduced the Cauchy variation to improve global searching ability and prevent premature convergence . The experimental results show that the improved algorithm has better search precision and convergence speed compared with other algorithms. [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]

This paper proposed a hybrid flower pollination algorithm (refrHFPA) based on refraction principle for the slow convergence rate of flower pollination algorithm and low optimization accuracy. The algorithm firstly used the harmony search algorithm to improve the convergence speed of the algorithm, then it used the refraction principle to improve the diversity of the population, and helped the algorithm to jump out of the local optimal and improved the accuracy of optimization. It used eight test functions to compare other intelligent algorithms, and the results show that the refrHFPA algorithm has a significant improvement in convergence speed and optimization accuracy. [ABSTRACT FROM AUTHOR]