Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real‐world planning problems such as multi‐machine engineering system (MES). Population‐based algorithms such as genetic algorithm (GA) and simulated annealing (SA) algorithm are only used to solve small‐ or medium‐scale cases in which the city number is <2000. Furthermore, in term of the convergence speed and solution quality, their performances have further improvement space. Since many real‐world problems can be modelled by large‐scale CTSP, it is necessary to study better algorithms to solve large‐scale CTSP. Since artificial bee colony algorithm (ABC) can show good performance in solving combination optimisation problems, and therefore this study applied improved ABC algorithms to solve large‐scale CTSP. The modified ABC algorithms use generating neighbourhood solution (GNS) to improve ABC for this problem, two limitation and optimisation conditions are applied into GNS during the process of reinserting cities. The extensive experiments verify that the improved algorithms can demonstrate better solution quality than the compared algorithms for large‐scale CTSP.

Activity prediction aims to predict what activities will occur in the future. In smart homes, to facilitate the daily living of the residents, automated or assistive services are provided. To provide these services, the ability of activity prediction is necessary. When we make a prediction, most of the existing works focus on predicting information about the next activity. However, in a smart home environment, compared with just predicting information about the next activity, another type of activity prediction problem has more practical value: predicting what activities will occur in the coming time period of a certain length. The necessity of this type of prediction is due to the purpose of the smart homes and the character of the activities. Many activities in smart homes need preparation time before being performed. Through this type of prediction, activities could be predicted sufficient time before being performed, and there will be adequate time for the smart home system to prepare corresponding automated or assistive services. As more than one activity could occur within the time period in which the prediction is made, this problem is a multilabel classification problem. In this article, we first give a formulation of the problem. Then, we propose a deep learning model to address it. The proposed model consists of the convolutional part, the long short-term memory layer, and the multilabel output layer. Experiments on real-world datasets show the effectiveness of our model.

The Coloured Travelling Salesman Problem (CTSP), a generalised version of the Multiple Travelling Salesman Problem (MTSP), has been proposed to model some real-world applications. This work proposes a discrete ITÔ (DITÔ) algorithm to solve CTSP. It combines the continuous ITÔ stochastic process with Ant Colony Optimisation (ACO) algorithm. First, the stochastic drift and volatility terms of ITÔ process are designed to be suitable for solving combinatorial optimisation problems, such as TSP and CTSP. And then, inspired by ACO, the generative model for a feasible solution of CTSP is constructed via the distance between cities and the experience gathered in the searching process. The experiments results show that the performance of DITÔ is superior to some state-of-the-art meta-heuristic algorithms in terms of the quality of the solution and computational time.

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000 dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid ITÖ (HITÖ) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITÖ. In the iteration process of HITÖ, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITÖ is superior to the known comparison algorithms in terms of the quality solution.