For solving the problem of multi-objective community detection in complex network, a Discrete Random Drift Particle Swarm Optimization (DRDPSO) algorithm was proposed. Firstly, by performing random coding operation on communities and using discretization operation for random drift optimization algorithm, the local network structure was improved and the global modularity value was gradually enhanced. Secondly, two objective functions, Kernel K-Means (KKM) and Ratio Cut (RC), were used to control the community size in the network and ameliorate the modularity resolution ratio. Finally, the Pareto non-inferior solution sets were updated step by step according to the multi-objective solving strategy, and the objective community structures satisfying the requirements were selected from the Pareto non-inferior solution sets. To verify the effectiveness of proposed algorithm, the comparison experiments of DRDPSO algorithm with other community detection algorithms were carried out on three generation networks with 10 different parameter configurations and three real networks. And the community detection results obtained by different algorithms were compared and analyzed by using two evaluation indicators of best community. Experimental results show that using DRDPSO algorithm for solving the multi-objective community detection problem in complex network, the probability of obtaining the highest community detection evaluation indexes (normalized mutual information and modularity) is more than 95%. The application of DRDPSO algorithm in real network can further improve the accuracy and robustness of network community division.

Aiming at the nonlinear design optimization problems with multiple constraints in the field of engineering shape design, an Adaptive Gaussian Quantum-behaved Particle Swarm Optimization (AG-QPSO) algorithm was proposed. By adjusting the Gaussian distribution adaptively, AG-QPSO algorithm was able to have strong global search ability at the initial stage of search process, and with the search process continued, the algorithm was able to have stronger local search ability, so as to meet the demands of the algorithm at different stages of the search process. In order to verify the effectiveness of the algorithm, 50 rounds of independent experiments were carried out on the two engineering constraint optimization problems: pressure vessel design and tension string design. The experimental results show that AG-QPSO algorithm achieves the average result of 5 890.931 5 and the optimal result of 5 885.332 8 on the pressure vessel design problem, and achieves the average result of 0.010 96 and the optimal result of 0.010 96 on the tension string design problem, which are better than the results of the existing algorithms such as the standard Particle Swarm Optimization (PSO) algorithm, Quantum Particle Swarm Optimization (QPSO) algorithm and Gaussian Quantum-behaved Particle Swarm Optimization (G-QPSO) algorithm. At the same time, the small variance of the results obtained by AG-QPSO algorithm indicates that the algorithm is very robust.