Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.

Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.

The Internet of Vehicles (IoV) is the extension of the Internet of Things (IoT) technology in the field of transportation systems. Ride‐sharing is one of intelligent travel applications in IoV. Ride‐sharing is aimed at taking passengers with similar itineraries and time arrangements to travel in the same car according to a certain matching rule. To effectively integrate transport capacity resources and reduce the number of cars on the road, ride‐sharing has become a popular and economical way of travel. The matching and optimizing of drivers and passengers are the core contents of a ride‐sharing application system. This paper mainly studies the dynamic real‐time matching of passengers and drivers in IoV, considering the main factors such as travel cost, car capacity, and utility. The matching problem is formulated in a ride‐sharing system as a Role‐Based Collaboration (RBC). A new utility method for the matching optimization of ride‐sharing is present. In this paper, we establish a model for simulating the assignment of ride‐sharing with the help of the Environments‐Classes, Agents, Roles, Groups, and Objects (E‐CARGO) model. The objective function and formal definitions are proposed. The utility and time of optimal matching are obtained by using the Kuhn‐Munkres algorithm on the revenue matrix. The experimental results show that the proposed formal method based on the E‐CARGO model and utility theory can be applied in the ride‐sharing problem. Numerical experiments show that the matching time cost increases with the increase of the number of drivers and passengers participating in the ride‐sharing system. When the number of drivers and passengers is different, one‐to‐many matching takes the least time, and one‐to‐one matching takes more time. When the number of drivers and passengers is the same, the time cost of one‐to‐one matching increases sharply with a certain value (bigger than 800). Compared with other matching methods, the time spent by the one‐to‐many method is reduced by 30%. The results show that the proposed solution can be applied to the matching and pricing in a ride‐sharing system.