Large-scale optimization problems (LSOPs) have attracted increasing attention in the big data era. Recently, matrix-based evolutionary computation (MEC) has been proposed as a new diagram for solving LSOPs within a short running time. However, compared with its corresponding classical non-matrix-based evolutionary computation (EC) algorithm, the MEC has not yet essentially improved its problem-solving ability or convergence speed. Therefore, how to enhance the problem-solving ability and convergence speed of MEC while maintaining its advantage of fast computational speed (i.e., short running time) has become a significant research topic. With this concern, this paper proposes a novel random matrix-based particle swarm optimization (RMPSO) algorithm, together with two novel designs. Firstly, a random matrix-based learning strategy is proposed, which enables each particle to learn from its superior particles by utilizing random matrices. Therefore, the RMPSO can effectively enhance the global search efficiency of the particles to improve problem-solving ability while maintaining fast computational speed. Secondly, a matrix-based knowledge and data-driven analysis strategy based on the dynamic system theory is proposed to analyze the algorithm convergence of RMPSO and help configure its parameters for fast convergence speed. To evaluate the proposed RMPSO, extensive experiments are conducted using 20 LSOPs, including 12 scalable problems with up to 10,000 dimensions and all the 8 multimodal LSOPs used in the latest IEEE CEC Large-Scale Global Optimization competition. Experimental results show that the RMPSO outperforms the compared state-of-the-art algorithms, including the champion algorithms from the LSGO competition, particularly on LSOPs with many local optima.

This paper develops a new Tourist Route Planning (TRP) model by incorporating the entrance fees and the experience values of scenic spots, the travelling costs between scenic spots, and the budget of the tourist. Resultantly, the new TRP aims at finding an optimal route by maximizing the travelling experience value of the tourist with the constraint that the total cost of the route including the travelling costs and the spot entrance fees does not exceed the given budget. To effectively solve this new TRP, this paper adapts the five classical ant colony optimization algorithms (ACO), namely ant system (AS), elite AS (EAS), rank-based AS (RAS), max-min AS (MMAS), and ant colony system (ACS). To this end, this paper first introduces a new heuristic information measure by integrating the experience values and the entrance fees of the scenic spots, and the traveling costs between scenic spots. Further, a new local search strategy encompassing 2-opt and one spot insertion operator is designed to further improve the quality of the route under the budget constraint. Abundant experiments have been carried out on various TRP instances of three scales, namely small-scale, medium-scale, and large-scale, involving different numbers of scenic spots and different settings of budgets. The experimental results demonstrate that all the adapted five ACO algorithms are very effective for addressing the new TRP. Among them, RAS performs the best on small-scale TRP instances, and ACS obtains the best results on medium-scale TRP instances, while MMAS is the most effective one in addressing large-scale TRP instances.

Leading exemplars play significant roles in updating particles to seek optimal solutions for Particle Swarm Optimization (PSO). Along this road, this paper devises an Individual-level Dominant Exemplar Selection (IDES) framework for PSO, giving rise to a new PSO variant named IDESPSO. Specifically, instead of using their own personally best positions and the globally best position of the entire swarm to update particles, IDES first randomly chooses two different exemplars for each particle from all personally best positions. Then, it compares the two selected exemplars with the personally best position of this particle. Based on the comparison results, different updating strategies are utilized to update different particles. This method notably enriches the variety among the chosen leading exemplars, thereby substantially bolstering the updating diversity of particles. Under IDES, this paper further develops seven selection strategies to help IDESPSO pick up promising exemplars for particles to evolve. Specifically, the seven selection schemes are the roulette wheel selection, the tournament selection, and five hybridizations of two basic models. A series of experiments have been undertaken on the universally used CEC2014 problem suite to compare IDESPSO with the seven selection schemes and two classic PSOs. The empirical results show that IDESPSO paired with anyone of the seven selection methods, markedly outperforms the two classical PSO variants, highlighting its significant performance.

Ant selection for updating the pheromone is one most crucial operation in ant colony optimization (ACO). In this direction, this paper designs an adaptive ant selection strategy (AAS) to adaptively and dynamically select ants to update the pheromone for ACO. Therefore, a new ACO, called AAS-ACO is developed. Specifically, AAS-ACO first assigns a non-linear selection probability to each ant based on its path ranking. As a result, better ants preserve exponentially higher selection probabilities. Then, based on the selection probabilities, ants are adaptively selected for the pheromone update. By this means, on the one hand, the number of ants involved in the pheromone update is uncertain; on the other hand, relatively better ants instead of absolutely better ones are adaptively selected to update the pheromone, leading to the promotion of search diversity. Subsequently, a dynamic weighting strategy is designed to adjust the amount of the pheromone deposited by the best ant in the current iteration to enhance the search convergence. With the two schemes, AAS-ACO is expected to maintain a suitable compromise between search diversity and search convergence to seek the optimal solutions to TSP. Experiments on 10 classical TSP instances varying from 100 to 1000 cities have proven the significant superiority of AAS-ACO to 5 classic ACOs, especially on high-dimensional TSP problems.

Music is an important way for emotion expression, and traditional manual composition requires a solid knowledge of music theory. It is needed to find a simple but accurate method to express personal emotions in music creation. In this paper, we propose and implement an EEG signal‐driven real‐time emotional music generation system for generating exclusive emotional music. To achieve real‐time emotion recognition, the proposed system can obtain the model suitable for a newcomer quickly through short‐time calibration. And then, both the recognized emotion state and music structure features are fed into the network as the conditional inputs to generate exclusive music which is consistent with the user’s real emotional expression. In the real‐time emotion recognition module, we propose an optimized style transfer mapping algorithm based on simplified parameter optimization and introduce the strategy of instance selection into the proposed method. The module can obtain and calibrate a suitable model for a new user in short‐time, which achieves the purpose of real‐time emotion recognition. The accuracies have been improved to 86.78% and 77.68%, and the computing time is just to 7 s and 10 s on the public SEED and self‐collected datasets, respectively. In the music generation module, we propose an emotional music generation network based on structure features and embed it into our system, which breaks the limitation of the existing systems by calling third‐party software and realizes the controllability of the consistency of generated music with the actual one in emotional expression. The experimental results show that the proposed system can generate fluent, complete, and exclusive music consistent with the user’s real‐time emotion recognition results.

EEG channel selection aims to choose informative and representative channels to reduce data redundancy. It is very beneficial for improving the utility and efficiency of emotion recognition. Previous studies on EEG channel selection have not considered the influence of genders despite long-standing belief in gender differences with respect to emotion analysis. In this paper, we collected EEG signals from 20 subjects containing 10 males and 10 females by letting them watch short emotional videos. Then, to reduce data redundancy, we propose an enhanced genetic algorithm to select the optimal channel subsets separately for male and female subjects by incorporating a novel evolution operation. Experimental results show that the proposed algorithm achieves higher accuracy in terms of emotion recognition than several compared methods with a smaller channel subset. Besides, experimental results also indicate that the gender differences in neural patterns indeed exist. Through this study, the gender-sensitive channel selection offers a new avenue for further development of EEG based emotion recognition.