Swarm intelligence labor division refers to any algorithm and distributed problem solving method that is inspired by the collective behaviors of social insects and other animal groups. It can be widely used in real-life task assignment. Focusing on the task assignment problem like traffic signal timing, the theory of labor division that describes the interaction mode between bee individuals was introduced, a Bee colony Double Inhibition Labor Division Algorithm (BDILDA) based on swarm intelligence was proposed, in which the dynamic accommodation of swarm labor division was achieved through interaction between internal and external inhibitors of the individual. In order to verify the validity of BDILDA, the traffic signal timing problem was selected for simulation experiments. BDILDA was used to solve actual case of traffic signal timing and the result was compared with the results of Webster algorithm, Multi-Colony Ant Algorithm (MCAA), Transfer Bees Optimizer (TBO) algorithm and Backward FireWorks Algorithm (BFWA). The experimental results show that average delay time of BDILDA is reduced by 14.3-20.1 percentage points, the average parking times is reduced by 3.7-4.5 percentage points, the maximum traffic capacity is increased by 5.2-23.6 percentage points. The results indicate that the proposed algorithm is suitable for solving dynamic assignment problems in uncertain environment.

Concerning that the existing researches on public opinion propagation model are seldom combined with the practical opinion data and digging out the inherent law of public opinion propagation from the opinion big data is becoming an urgent problem, a parameter inversion algorithm of public opinion propagation model using neural network was proposed based on the practical opinion big data. A network opinion propagation model was constructed by improving the classical disease spreading Susceptible-Infective-Recovered (SIR) model. Based on this model, the parameter inversion algorithm was used to predict the network public opinion's trend of actual cases. The proposed algorithm could accurately predict the specific heat value of public opinion compared with Markov prediction model.The experimental results show that the proposed algorithm has certain superiority in prediction and can be used for data fitting, process simulation and trend prediction of network emergency spreading.

To solve the prematurity of evolutionary algorithm on the equilibrium constrained circles packing problem, the Multi-Quantum States Quantum-Inspired Evolutionary Algorithm (MQSQIEA) proposed in this paper was beneficial to keeping the population diversity, which combined the heuristics based on the order-based positioning technique. The layout sequence was optimized efficiently by MQSQIEA. The solving speed was improved by the multi-quantum states coding and the convergence criterion based on the average convergence probability. The observation method based on the taboo strategy and heuristic information was introduced to obtain the n-ary solution with different integers and ensure the priority to place circles with large mass and long radius. The dynamic quantum evolutionary strategy was applied to guide the population to evolve towards the best individual. The positioning probability function introduced to the positioning rule was employed to improve the solution quality. The numerical experimental results show that the proposed method can effectively solve the circular packing problem with equilibrium constraints.

Task assignment problem is a typical NP problem. Particle Swarm Optimization (PSO) algorithm was used to solve task assignment problem. The model of task assignment problem was formulated and the detailed solution for solving task assignment problem based on PSO algorithm was illuminated. To get better optimization results, an improved PSO algorithm named IPSO including variance mechanism and local updating mechanism was presented. Examples and simulation experiences demonstrate that the IPSO algorithm is effective in solving task assignment problem.

A new algorithm architecture was proposed. In order to adjust effectively the ratio of exploration subgroup versus exploitation one, the algorithm adopted the centralized processing technique to construct the local environment factor, and balanced the local and global search capabilities of the algorithm. Furthermore, compared with other improved intelligent algorithms, experimental results got from the application and verification of real constrained engineering design problem indicate that the algorithm performs better in terms of accuracy, efficiency and robustness.