The single channel Convolutional Neural Network (CNN) cannot fully study the feature information of text with a single perspective. In order to solve the problem, a new Dual-Channel CNN (DCCNN) algorithm was proposed. Firstly, the word vector was trained by Word2Vec, and the semantic information of sentence was obtained by using word vector. Secondly, two different channels were used to carry out convolution operations, one channel was the character vector and the other was the word vector. The fine-grained character vector was used for assisting word vector to capture deep semantic information. Finally, the convolutional kernels of different sizes were used to find higher-level abstract features within the sentence. The experimental results show that, the proposed DCCNN algorithm can accurately identify the sentiment polarity of text, its accuracy and F1 value are above 95%, which are significantly improved compared with the algorithms of logistic regression, Support Vector Machine (SVM) and CNN.

To overcome the demerits of basic Shuffled Frog Leaping Algorithm (SFLA), such as slow convergence speed, low optimization precision and falling into local optimum easily, a novel survival of the fittest SFLA with normal mutation was proposed. In the local search strategy of the proposed algorithm, the normal mutations for updating strategy of the worst frog individuals in the subgroup were introduced to avoid the algorithm falling into local convergence effectively, expand the searching space and increase the diversity of population. Meanwhile, the mutations were selected for a small number of worse frog individual in the subgroup to inherit the useful mutations instead of the bad mutations. The survival of the fittest was implemented, the quality of the population was improved, the blindness of the algorithm optimization process was reduced and the algorithm optimization was speeded up. The elite mutation mechanism for the best frog individuals in each subgroup was introduced for obtaining better individuals to enhance the global optimization ability of the algorithm further, avoid falling into local convergence, and lead the whole population evolution to the better. The experimental results of 30 independent runs indicate that the proposed algorithm can converge to the optimal solution of 0 in Sphere, Rastrigrin, Griewank, Ackley and Quadric, which is better than the other contrastive algorithms. The experimental results show that the proposed algorithm can avoid falling into premature convergence effectively, improve the convergence speed and convergence precision.

For Resource-Constrained Project Scheduling Problems (RCPSP), the Cooperative Shock search Particle Swarm Optimization with Chaos (CSCPSO) was proposed. On the basis of particle attractor, a bidirectional cooperation shock search mechanism was established in the algorithm to enhance the search accuracy and diversity of population. The particles converged to particle attractor, meanwhile they adjusted the dimensions whose adjacent relationship were inconsistent with attractor's by shock search in the mechanism. Combined with topological sorting based on particles and serial schedule generation scheme, the gotten scheduling scheme could meet the project schedule constraints of resource and precedence relations. The tests on specific examples show that the proposed algorithm can get higher accuracy and better stability for RCPSP.