The latent factor analysis (LFA) model has been widely used to uncover latent relationships from high-dimensional sparse (HiDS) matrices. However, the performance of LFA depends largely on the hyper-parameter value used in the model training. Traditional hyper-parameter tuning methods such as grid search suffer from inefficiency and inaccuracy. In recent years, the particle swarm optimization (PSO) algorithm offers an intelligent approach to adaptively adjust the hyper-parameter of LFA. However, the global optimal solution of the hyper-parameter tuning problem is not fixed due to its dynamic decision space. Therefore, it is difficult for PSO to determine the best hyper-parameter for each training iteration. To address this problem, this paper proposes a novel hyper-parameter adaptive adjustment algorithm called dynamic stochastic reorientation PSO (DSR-PSO) that adapts to constantly changing decision spaces. By randomly adjusting the search directions of particles and perturbing the elite particles, the dynamic property of the DSR-PSO can be enhanced, so that the hyper-parameter can be adjusted in real time throughout the model training process. Furthermore, this paper proves the convergence of the DSR-PSO and gives its convergence condition by discussing the distribution of the characteristic roots. Finally, this paper proposes the DSR-PSO-based LFA (DPL) model by incorporating the DSR-PSO-based hyper-parameter adjustment into the LFA to promote its model training, and analyzes its complexity. Experimental results on benchmark datasets show that the proposed DPL surpasses state-of-the-art LFA models in terms of accuracy and efficiency.

The latent factor analysis (LFA) model is an effective tool for extracting valuable information from high-dimensional and sparse (HiDS) matrices. However, traditional LFA usually suffers from low accuracy due to the limitations of the stochastic gradient descent (SGD) algorithm used in its model training. First, the learning rate of SGD is adjusted manually, which greatly affects the training efficiency. A recent solution is adjusting this hyperparameter by the particle swarm optimization (PSO) algorithm. However, PSO cannot adapt well to the dynamic decision space of this problem due to its strong convergence. Second, SGD relies solely on the gradient information to perform the optimization, which decreases the training accuracy. To address the above two issues, this article proposes a novel LFA model called genetic algorithm-based two-step LFA (GA-TSLFA), which employs the GA to facilitate the model training. Compared to PSO, the GA has better flexibility, which can be employed to tune the hyperparameter of LFA in dynamic decision spaces and refine the model in high-dimensional and complex decision spaces by designing suitable evolutionary operators. The training of the proposed GA-TSLFA consists of two steps. In the first step, the model is pretrained by SGD whose learning rate is adaptively adjusted by a proposed GA. In the second step, the LF matrices generated by SGD are further refined using a proposed GA-based framework. This framework operates by optimizing a subset of partial vectors, which are selected through a dedicated strategy. In this way, the model's accuracy can be further enhanced. Empirical studies on benchmark datasets show that the GA-TSLFA surpasses state-of-the-art LFA models in prediction accuracy and has a competitive efficiency.

In latent factor analysis (LFA), stochastic gradient descent (SGD) is a widely used algorithm to decompose high-dimensional sparse matrices. However, traditional SGD relies on manually tuning the learning rate which is a vital hyper-parameter to influence its optimization. To improve the performance of LFA, particle swarm optimization (PSO) has been employed to automatically adjust the learning rate of SGD. However, the classical PSO algorithm can not track the movement of the optimal hyper-parameter setting during its convergence process and suffers from the hysteresis due to its individual update scheme. To solve this problem, this paper proposes an algorithm called enhanced dynamic gradient particle swarm optimizer (EDG-PSO) which can achieve the dynamic global optimization of the learning rate during the model training process of LFA. In EDG-PSO, the directions of particles are updated with a certain probability through calculating their gradient’s changes. Moreover, the incorporation of the Adam algorithm further improves the search efficiency of PSO. By embedding the proposed hyper-parameter tuning algorithm into the SGD-based LFA, the EGD-PSO-incorporated LFA (EPLFA) model is proposed by this paper. The experimental results show that the proposed EPLFA model has a higher prediction accuracy and a lower computational cost than existing adaptive adjustment LFA models.

This paper fully considers the influence of external light intensity and temperature changes on the output power of the photovoltaic (PV) power generation system. Based on the sliding mode control (SMC) and traditional perturbation observation (P&O) method, an improved double closed-loop control strategy is proposed for the maximum power point tracking (MPPT) of the PV system. Firstly, according to the traditional SMC idea, the SM surface considering the adaptive P&O offset compensation, is designed according to the trajectory of the characteristic output curve of the PV panel. Then, the stable area is derived, and the optimal parameters are designed based on the Lyapunov stability theorem. Finally, different control strategies are analyzed by simulation to verify the correctness and effectiveness of the proposed method.

Community detection in multiplex networks is an emerging research topic in the field of network science. Existing methods usually ignore the similarities among component layers of a multiplex network when detecting its community structures, which decreases the detection efficiency. In this article, we decompose the community detection in multiplex networks into two problems and propose a novel algorithm that can detect both the specific community partition for each component layer (layer-level community structure) and the composite community structure shared by all layers. First, by specifying the modularity optimization on a network layer as an optimization task, we model the layer-level community detection as a multitask optimization (MTO) problem and employ an evolutionary MTO algorithm to solve it. In this way, the topology correlations among different layers can be utilized to facilitate the community detection. Second, we propose an evolutionary clustering ensemble method to find the composite community structure based on the layer-level community partitions and the multiplex network. The proposed method is tested on both synthetic and real-world benchmark networks and compared with classical and state-of-the-art algorithms. Experimental results show that the proposed algorithm has superior community detection performances on multiplex networks.

The multiple-preys pursuit (MPP) is the adversarial game between predators and preys. If the capture of a prey is defined as that it cannot move anymore due to the surrounding of predators, there are two kinds of task allocations. One is about assigning which prey to which group of predators so that all preys can be captured. The other is about assigning which capturing position to which predator to encircle the prey simultaneously. In this paper, the MPP is modeled as a dynamic optimization problem and each its time step is solved in two stages. Firstly, the first kind of task allocation problem is modeled as the biquadratic assignment problem (BiQAP) and a MPP fitness function is proposed for the evaluation of such BiQAP task allocations. In this way, the MPP is transformed to several single-prey pursuit (SPP) problems. Secondly, for each SPP, we extend the coordinated SPP strategy CCPSO-R (cooperative coevolutionary particle swarm optimization for robots) to its parallel version as PCCPSO-R to enable the parallel implicit capturing position allocating by parallel observation, decision making, and moving of predators. Through experiments of the current BiQAP solvers on the task allocation, we improve the best one of them in statistic based on the domain knowledge. Moreover, the advantages of PCCPSO-R in the capturing efficiency over CCPSO-R is testified in the MPP experiments.

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.