An improved user grouping and power allocation strategy was proposed for high complexity problem of optimal user grouping and power allocation schemes for Non-Orthogonal Multiple Access (NOMA) systems. Firstly, the users were grouped, the first user of each subchannel was determined by channel gain value, and the remaining users were allocated by greedy matching method. Then, the power of user was allocated, and the power allocation problem was divided into two parts: inter-subchannel and intra-subchannel. The power was allocated by the linear water-filling algorithm for inter-subchannels, and the power was allocated by the proposed iterative power allocation algorithm for intra-subchannels. Finally, a Lagrangian function was constructed to maximize the throughput of system under the constraints of maximizing transmit power and guaranteeing the minimum data rate for each user. The simulation results show that in the case of multiple users, compared with the LWF-FTPA (Linear WaterFilling-Fractional Transmit Power Allocation) algorithm and EQ-FTPA (EQual-Fractional Transmit Power Allocation) algorithm, the proposed strategy has system throughput increased by 8% and 20% respectively, indicating that the strategy is better than traditional algorithms.

In order to improve the diversity of population and the convergence performance of algorithm, a Scheduled competition learning based Multi-Objective Particle Swarm Optimization (SMOPSO) algorithm was proposed. The multi-objective particle swarm optimization algorithm and the competition learning mechanism were combined and the competition learning mechanism was used in every certain iterations to maintain the diversity of the population. Meanwhile, to improve the convergence of algorithm without using the global best external archive, the elite particles were selected from the current swarm, and then a global best particle was randomly selected from these elite particles. The performance of the proposed algorithm was verified on 21 benchmarks and compared with 8 algorithms, such as Multi-objective Particle Swarm Optimization algorithm based on Decomposition (MPSOD), Competitive Mechanism based multi-Objective Particle Swarm Optimizer (CMOPSO) and Reference Vector guided Evolutionary Algorithm (RVEA). The experimental results prove that the proposed algorithm can get a more uniform Pareto front and a smaller Inverted Generational Distance (IGD).

The skills level of employees has a great influence on the execution efficiency of Field Service Scheduling Problem (FSSP). Employee skill factors are not considered in the existing research. To solve the problem, firstly, taking the travel time, service time and waiting time of staff as optimization goals, the FSSP model considering the skill level of staff was established. Then, a Hybrid Fruit fly Optimization Algorithm (HFOA) was proposed to optimize the model. Based on the features of the problem and the merits of the algorithm, an encoding method based on the matrix was designed. Two operators of matrix were defined based on the theory of swarm intelligence, and then three search operators were proposed, and the smell-based search strategy and the vision-based search strategy of Fruit fly Optimization Algorithm (FOA) were redesigned. At the same time, in order to improve the algorithm's performance, an initialization operator based on the nearest insertion heuristic algorithm was constructed. Finally, the simulation experiment was carried out through typical instances and the proposed algorithm was compared with Genetic Algorithm (GA) and Greedy Randomized Adaptive Search Procedure (GRASP) algorithm. The experimental data show that HFOA performs better in terms of mean value and optimal value than the other two algorithms. The results show that HFOA outperforms other algorithms in terms of optimization accuracy and stability after improving the initialization method and search strategy.

Focusing on the slow convergence and the poor uniformity of multi-objective differential evolution algorithms when solving multi-objective optimization problems, a Multi-Objective Differential Evolution algorithm with Improved Ranking-based Mutation (MODE-IRM) was proposed. The optimal individual involved in the mutation was used as the base vector, which accelerated the resolving speed of the ranking-based mutation operator. In addition, a strategy of opposition-based parameter was adopted to dynamically adjust the values of parameters in different optimization stages, so the convergence rate was further accelerated. Finally, an improved crowding distance calculation formula was introduced in the sort operation, which improved the uniformity of solutions. Simulation experiments were conducted on the standard multi-objective optimization problems including ZDTl-ZDT4, ZDT6 and DTLZ6-DTLZ7. MODE-IRM's overall performance was much better than MODE-RMO and other three algorithms of the PlatEMO including MOEA/D-DE (Multiobjective Evolutionary Algorithm based on Decomposition with Differential Evolution), RM-MEDA (Regularity Model-based Multi-objective Estimation of Distribution Algorithm) and IM-MOEA (Inverse Modeling Multi-objective Evolutionary Algorithm). Moreover, in terms of the performance metrics including GD (Generational Distance), IGD (Inverted Generational Distance) and SP (Spacing), the mean and variance of MODE-IRM on all problems were significantly less than those of MODE-RMO. The simulation results show that MODE-IRM has better performance in convergence and uniformity.

Traditional instance selection algorithms may remove non-noise samples by mistake and have low algorithm efficiency. For this issue, a genetic instance selection algorithm for K-Nearest Neighbor ( KNN) classifier was proposed. A two-stage selection mechanism based on decision tree and genetic algorithm was used in the algorithm. Firstly, the decision tree was used to determine the range of noise samples. Then, the genetic algorithm was used to remove the noise samples in this range precisely, which could reduce the risk of mistaken remove effectively and improve the algorithm efficiency. Secondly, the 1NN-based selection strategy of validation set was proposed to improve the instance selection accuracy of the genetic algorithm. Finally, the MSE (Mean Squared Error)-based objective function was used as the fitness function, which could improve the effectiveness and stability of the algorithm. Compared with PRe-classification based KNN (PR KNN), Instance and Feature Selection based on Cooperative Coevolution (IFS-CoCo), K-Nearest Neighbors ( KNN), the improvement in classification accuracy is 0.07 to 26.9 percentage points, 0.03 to 11.8 percentage points and 0.2 to 12.64 percentage points respectively, the improvement in AUC (Area Under Curve) and Kappa is 0.25 to 18.32 percentage points, 1.27 to 23.29 percentage points, and 0.04 to 12.82 percentage points respectively. The experimental results show that the proposed method has advantages in terms of classification accuracy and classification efficiency.

Hand-eye calibration based on nonlinear optimization algorithm can not guarantee the convergence of the objective function to the global minimum, when there are errors in both robot forward kinematics and camera external parameters calibration. To solve this tricky problem, a new hand-eye calibration algorithm based on quaternion theory by convex relaxation global optimization was proposed. The critical factor of the angle between different interstation rotation axes by a manipulator was considered, an optimal set of relative movements from calibration data was selected by Random Sample Consensus (RANSAC) approach. Then, rotation matrix was parameterized by a quaternion, polynomial geometric error objective function and constraints were established based on Linear Matrix Inequality (LMI) convex relaxation global optimization algorithm, and the hand-eye transformation matrix could be solved for global optimum. Experimental validation on real data was provided. Compared with the classical quaternion nonlinear optimization algorithm, the proposed algorithm can get global optimal solution, the geometric mean error of hand-eye transformation matrix is no more than 1.4 mm, and the standard deviation is less than 0.16 mm.

In order to effectively balance the global and local search performance of Particle Swarm Optimization (PSO) algorithm, an improved PSO algorithm based on Gaussian disturbance and natural selection (GDNSPSO) was proposed. Based on the simple PSO algorithm, the improved algorithm took into account the mutual influence among all individual best particles and replaced the individual best value of each particle with the mean value of them which contained Gaussian disturbance. And the evolution mechanism of survival of the fittest in natural selection was employed to improve the performance of algorithm. At the same time, the nonlinear adjustment of the inertia weight was adjusted by the cosine function with adaptive adjustment of the threshold of inertia weight and the adjustment strategy of the asynchronous change was used to improve the learning ability of the particles. The simulation results show that the GDNSPSO algorithm can improve the convergence speed and precision, and it is better than some recently proposed improved PSO algorithms.