No abstract

Recently, there has been a significant amount of work on the recognition of human emotions. The results of the work can be applied in real applications, for example in market survey or neuro-marketing. This interesting problem requires to recognize naturally human emotions which come from our mind but ignore the external expressions fully controlled by a subject. A popular approach uses key information from electroencephalography (EEG) signals to identify human emotions. In this paper, we proposed an emotion recognition model based on the Russell's circumplex model, Higuchi Fractal Dimension (HFD) algorithm and Support Vector Machine (SVM) as a classifier. Moreover, we also proposed a method to determine an emotion label of a series of EEG signals. Our model includes two main approaches in machine learning step. In a first approach, machine learning was utilized for all EEG signals from numerous subjects while another used machine learning for each particular subject. We extensively implemented our model in several test data. The experimental results showed that the first approach is impossible to apply in practical applications because EEG signal of each subject has individual characteristic. In addition, in the second, our model can recognize five basic states of human emotion in real-time with average accuracy 70.5%.

The Time Dependent Traveling Salesman Problem (TDTSP) is a class of NP-hard combinatorial optimization problems which has many practical applications. To the best of our knowledge, developing metaheuristic algorithm for the problem has not been studied much before, even though it is a natural and general extension of the Minimum Latency Problem (MLP) or Traveling Salesman Problem (TSP). In this paper, we propose an effective two-phase metaheuristic which combines the Insertion Heuristic (IH), Variable Neighborhood Search (VNS) and the tabu search (TS) to solve the problem. In a construction phase, the IH is used to create an initial solution that is good enough. In an improvement phase, the VNS is employed to generate diverse and various neighborhoods, while the main attribute of tabu search is to prohibit our algorithm from getting trapped into cycles, and to guide the search to escape local optima. Moreover, we introduce a novel neighborhoods’ structure in VNS and present a O(1) operation for calculating the cost of each neighboring solution in a special case of TDTSP where the TDTSP becomes the MLP. Extensive computational experiments on 355 benchmark instances show that our algorithm can find the optimal solutions for small instances with up to 100 vertices in a reasonable amount of time. For larger instances, our algorithm obtains the new best solutions in comparison with the state-of-the-art algorithm solutions.

Abstract. Multiple Traveling Repairmen Problem (MTRP) is a class of NP-hard combinatorial optimization problems. In this paper, an other variant of MTRP, also known as Multiple Traveling Repairmen Problem with Distance Constraint (MTRPD), is introduced. In MTRPD problem, a fleet of vehicles serves a set of customers. Each vehicle which starts from the depot is not allowed to travel any distance longer than a limit and each customer must be visited exactly once. The goal is to find the order of customer visits of all vehicles that minimizes the sum of all vertices' waiting time. To the best of our knowledge, the problem has not been studied much previously, even though it is a natural and practical extension of the Traveling Repairman Problem or Multiple Traveling Repairmen Problem case. In our work, we propose a metaheuristic algorithm which is mainly based on the principles of Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) to solve the problem. The GRASP is used to build an initial solution which is good enough in a construction phase. In a cooperative way, the VND is employed to generate diverse neighborhoods in an improvement phase, therefore, it can help the search escape from local optimal. Extensive numerical experiments on 321 benchmark instances show that our algorithm can find the optimal solutions with up to 50 vertices in several instances. For larger instances, our algorithm obtains provably near-optimal solutions, even for large instances.

Minimum Latency Problem (MLP) is a class of NP-hard combinatorial optimization problems which has many practical applications. In this paper, we investigate the global structure of the MLP solution space to propose a suitable meta-heuristic algorithm for the problem, which combines Tabu search (TS) and Variable Neighborhood Search (VNS). In the proposed algorithm, TS is used to prevent the search from getting trapped into cycles, and guide VNS to escape local optima. In a cooperative way, VNS is employed to generate diverse neighborhoods for TS. We also introduce a novel neighborhoods' structure for VNS and present a constant time operation for calculating the latency cost of each neighboring solution. Extensive numerical experiments and comparisons with the state of the art meta-heuristic algorithms in the literature show that the proposed algorithm is highly competitive, providing the new best solutions for several instances.