In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporati...
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In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs;for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model;during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum.
Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the se...
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Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.
evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best so...
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evolutionary multi-task optimization (EMTO) is an emerging research topic in the field of evolutionary computation, which aims to simultaneously optimize several component tasks within a problem and output the best solution for each task. Since EMTO has widespread applications in solving real-world multi-taskoptimization problems, in recent years, some EMTO algorithms have been proposed. However, most of which are based on the multifactorial evolution framework which has difficulties in independently controlling the optimization of each component task and implementing parallel computing. To tackle this problem and enrich the EMTO algorithms' family, this paper firstly designs a novel EMTO framework inspired by the brainstorming process of human beings when they solve multi-task problems. Under this framework, a novel EMTO algorithm, named as brain storm multi-taskoptimization (BSMTO), is presented, where the optimization for each component task and the knowledge transfer between different tasks are both implemented by the proposed brainstorming operations. Furthermore, through investigating the knowledge transfer process in the proposed algorithm, an enhanced BSMTO algorithm named as BSMTO-II is further proposed, where the knowledge transfer in each component task can be managed and controlled by our newly designed scheme. Finally, the proposed two algorithms are tested on benchmark problems. Experimental results show that BSMTO-II has a competitive performance compared with both classical and state-of-the-art algorithms. Moreover, the effectiveness of the proposed EMTO framework and the knowledge transfer control scheme is proved through experiments, and the key parameters settings and the algorithmic complexity are also discussed at last.
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