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In order to solve the problems of uneven resource utilization and long task completion time in complex task group scheduling, this paper constructed a complex task group resource scheduling model to minimize the mean square error of resource load and the task group completion time, and proposed a multi-objective optimization algorithm based on boundary range local search and NSGA-Ⅱ, called BRLSN. The algorithm used an effective coding method and cross mutation operator for iterative optimization, and constructed an elite retention strategy based on local search in boundary region to expand the search scope of the algorithm and preserved good individuals in the population. Experimental results show that compared with other multi-objective algorithms, the convergence and diversity of BRLSN are significantly improved. At the same time, the algorithm convergence speed is faster, the population quality is higher, and the result value of the final objective function is obviously optimized. [ABSTRACT FROM AUTHOR]

In order to fix the integer ambiguity quickly and accurately, this paper proposed a new ambiguity search algorithm based on measurement accuracy constraints to solve the problems of the LAMBDA algorithm, such as the wide search range of the integer ambiguity and the low search efficiency. When the least squares problem got optimally weighted, the algorithm took the carrier phase measurement accuracy as the constraint condition to test the fixed ambiguity solution on the basis of the stan-dard differential evolution （DE） algorithm. This algorithm solved the influence of different satellite altitude angles, and could achieve 99% success rate when solving 3D integer ambiguity. Compared with the MLAMBDA algorithm, the standard DE algorithm and the adaptive weighted differential evolution （AWDE） algorithm, the proposed algorithm further improves the efficiency and success rate of ambiguity resolution. [ABSTRACT FROM AUTHOR]

Targeting to the deficiency of sparrow search algorithm solved the multi-objective problems, and the problems can easily enter the partial optimization and inferior convergence in the counting process, this paper came up with a kind of improved multi-objective sparrow search algorithm. First of all, this paper used a new-type non-dominated sorting to find the Pareto front. Next, it integrated the polynomial mutation and cosine algorithm into species evolution strategy to strengthen its searching ability. It used the species selection method of competition mechanism to decrease the differentiation caused by partial optimized particle and overall optimized particle in the searching process. Finally, this paper compared the improved algorithm and various kinds of multi-objective algorithm in standard test function. The simulation results show that, the convergence and searching ability of the improved algorithm are all superior than other algorithm. Therefore, this algorithm can effectively address the multiple target optimization problem. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of aquila optimization algorithm（AO）, this paper proposed an aquila optimization algorithm using hybrid search strategy. Firstly, the algorithm introduced dynamic adjustment function to balance global exploration and local exploitation; Secondly, it introduced chaotic adaptive weights to enhance the global search capability of the algorithm and accelerate the convergence speed of the algorithm; Thirdly, it introduced a new individual mutation probability coefficient and an improved differential mutation strategy, and used individuals with better fitness values to lead other individuals in the population to carry out search activities, which maintained the diversity of the population and enhanced the ability of the algorithm to jump out of the local optimum. Through the numerical experiment simulation of 8 benchmark test functions, 10 CEC2019 test functions and 1 engineering application problem, The experimental results show that the algorithm has a significant improvement in global convergence speed and optimization accuracy, and has a better ability to jump out of the local optimum. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of normal SPBO, this paper analyzed the psychological characteristics of students ' learning, and put forward an improved student psychology based optimization algorithm ( HSSPBO) using hybrid strategy . Firstly, it took the inverse value of student ' s test scores as the fitness value of the student, and took the fitness value of the best students in the class as the benchmark, and then divided the students in the class into four categories : the best students, the good students, the ordinary students and the students who tried random improvement. Secondly, it used the dynamic switching probability of sine square and cosine square to balance global exploration and local development, so that the global exploration ability and local exploitation ability of the algorithm were improved effectively. Thirdly, by introducing Cauchy mutation strategy to change the local search step size, it improved effectively the local search ability of the algorithm and enhanced the ability of the algorithm to jump out of the local optimization. Finally, the HSSPBO algorithm used Levy flight strategy to make the individual search step length more random and flexible, which effectively enhanced the individual ' s searching ability, and further improved the optimization speed of the algorithm. Through simulation experiments of 12 benchmark functions and comparison with six optimization algorithms, the results show that HSSPBO ' s global search ability has been significantly improved, and it has faster global convergence speed, better optimization accuracy and stability in function optimization. [ABSTRACT FROM AUTHOR]

As many intelligent devices become the component of the Internet of Things system, the system' s architectural form and scale are showing diversified and huge characteristics . The performance of an IoT system depends on its own organizational structure . Therefore, the optimization of the organizational structure of the Internet of Things system is particularly important. Because the essence of the system architecture is the problem of the arrangement and combination of system nodes, this paper converted the system architecture problem into the optimization problem of the system utility value, and proposed a solution algorithm based on immune evolution. The algorithm used a hierarchical coding method to transform the system organization structure into an integer coding form. It designed an adaptive hierarchical crossover operator and as mall perturbation mutation operator, and used the degree of individual stimulation to guide the search direction of the algorithm and search for the algorithm that met the performance requirements. Experimental results show that this algorithm can obtain a system structure with high efficiency, and has excellent performance in success rate, relative error rate and convergence speed. [ABSTRACT FROM AUTHOR]