Computational intelligence (CI), including artificial neural network, fuzzy logic, and evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of algorithm for knowledge creation and problem solving, playing a significant role in CI and artificial intelligence (AI). However, traditional EC algorithms have faced great challenge of heavy computational burden and long running time in large-scale (e.g., with many variables) problems. How to efficiently extend EC algorithms to solve complex problems has become one of the most significant research topics in CI and AI communities. To this aim, this paper proposes a matrix-based EC (MEC) framework to extend traditional EC algorithms for efficiently solving large-scale or super large-scale optimization problems. The proposed framework is an entirely new perspective on EC algorithm, from the solution representation to the evolutionary operators. In this framework, the whole population (containing a set of individuals) is defined as a matrix, where a row stands for an individual and a column stands for a dimension (decision variable). This way, the parallel computing functionalities of matrix can be directly and easily carried out on the high performance computing resources to accelerate the computational speed of evolutionary operators. This paper gives two typical examples of MEC algorithms, named matrix-based genetic algorithm and matrix-based particle swarm optimization. Their matrix-based solution representations are presented and the evolutionary operators based on the matrix are described. Moreover, the time complexity is analyzed and the experiments are conducted to show that these MEC algorithms are efficient in reducing the computational time on large scale of decision variables. The MEC is a promising way to extend EC to complex optimization problems
Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. In this paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. First, we combine a standard epidemic dynamic, the susceptible–exposed–infected–recovered (SEIR) model with population migration. This model can provide a biological spread process for emerging infectious diseases. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. Moreover, an objective function is defined to minimize the error based on these data. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method.
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