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Sequential databases have wide applications, such as market basket analysis, medical prediction, and sign language recognition. Most prior research is based on pointed-based sequential databases, which assume each item/event occurs instantaneously. However, in many real-world scenarios, events persist over intervals of varying durations, such as varying time intervals of a symptom or a gesture of sign language. Assigning the same weight to different times of events and neglecting the duration of events can hinder the recognition of interesting patterns, such as concurrent symptoms preceding a disease. To address these issues, this paper integrates duration with temporal patterns in interval-based sequential databases, introduces the concept of temporal duration-based patterns (TDPs), and designs two algorithms called FTDPMiner-EP (Frequent TDPMiner based on endpoint representation) and FTDPMiner-TM (Frequent TDPMiner based on triangular matrix representation) by using different extension methods to mine frequent TDPs. Due to the complex relationships between events, temporal pattern mining is more challenging than sequential pattern mining. Strategies are used in this paper to accelerate the algorithms' search process. Experiments are conducted on both real and synthetic databases, which show good performance of the two algorithms. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient and powerful population-based search algorithm for solving numerical optimization problems in continuous spaces. It has been proven that multi-strategy DE algorithms are more effective than single-strategy DE algorithms in addressing benchmark and real-world problems. However, most multi-strategy DE variants focus on maintaining population diversity and balancing exploitation and exploration, ignoring the dynamic allocation of computational resources. Moreover, the success of these algorithms often depends on additional designed techniques, leading to increased computational complexity. In this paper, the Collaborative Resource Allocation-based Differential Evolution (CRADE) is introduced. It involves a collaborative resource allocation mechanism that utilizes the historical performance ranking of three mutation strategies to automatically allocate computational resources to various subpopulations during the search process. The parameter adaptation technique is used to adjust the associated control parameters of different mutation strategies. As a result, the most efficient mutation strategy consumes the majority of computational resources at different search stages to mitigate inefficient search under constrained resources. The performance of CRADE is evaluated on the well-known CEC2013 benchmark function set. The paper also investigates its application in the parameter identification of photovoltaic solar cells and modules. The overall results show that CRADE exhibits superior and competitive performance compared to other state-of-the-art algorithms. Consequently, CRADE has emerged as a novel and effective approach for addressing numerical optimization problems, distinguished by its excellence, practicality, and unwavering reliability. [ABSTRACT FROM AUTHOR]

As the generalization of fuzzy covering, fuzzy β covering can effectively deal with uncertain information in hybrid data. Swarm intelligence algorithm has unique advantages in feature selection. This paper proposes a novel cooperative swarm intelligence feature selection method for hybrid data based on fuzzy β covering and fuzzy self-information. First, the parameterized fuzzy β neighborhood and fuzzy decision in a fuzzy β covering decision information system are defined, and the lower and upper fuzzy approximations are further studied. Fuzzy rough set model exclusively focuses on the information derived from the lower fuzzy approximation within a decision. In actual circumstances, the uncertainty of fuzzy information is linked to not only the lower fuzzy approximation but also the upper fuzzy approximation. Then, by incorporating the lower and upper fuzzy approximations of a decision, fuzzy self-information and relative fuzzy self-information are introduced as two evaluation indicators of feature selection. Next, a forward feature selection algorithm based on heuristic search strategy is designed by means of these two evaluation indicators. Moreover, in order to effectively explore feature subsets to a greater extent, a cooperative swarm intelligent feature selection algorithm based on random search strategy is also designed by combining particle swarm optimization algorithm and cuckoo search algorithm. Finally, an array of experiments is executed to compare two designed algorithms with five other existing feature selection algorithms. Experimental results show that these two designed algorithms achieve a high feature selection rate while improving the classification accuracy of the raw data. [ABSTRACT FROM AUTHOR]

• An improved gaining-sharing knowledge-based algorithm named MSGSK is proposed. • A parameter adjustment strategy is developed to adjust relative parameters of MSGSK. • A backtracking differential mutation strategy is designed to enrich the population diversity. • A strategy selection mechanism is presented to integrate the former two strategies. • MSGSK extracts more accurate parameters for five PV models. The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature. [ABSTRACT FROM AUTHOR]

Cloud-assisted biometric identification can benefit data owners with limited resources by offloading their storage and identification tasks to cloud servers with abundant resources. However, the adoption of this computing paradigm is hindered by security and privacy concerns. To solve the dilemma, several privacy-preserving cloud-assisted biometric identification protocols have been proposed. Nevertheless, the existing designs suffer from one or more of the following restrictions: (1) support only for data owner-online query, (2) low security level, and (3) inefficiency. Motivated by these challenges, this paper aims to explore a more secure and efficient construction. Concretely, we initialize the error weighted hashing (EWH)-based data packing technique to replace the time-consuming linear scan search during the identification process. Additionally, to ensure compatibility with the EWH-based search algorithm, we formulate an identity (id)-based scoring strategy and introduce a random split-based data encryption algorithm, which circumvents the costly homomorphic encryption scheme. Within the framework of the two non-colluding servers model, the random split-based one-time pad encryption guarantees provably indistinguishable security against chosen-plaintext attacks. Finally, since only simple hash and xor operations are involved, our novel construction exhibits significantly improved performance compared to prior designs, both in theory and in practice. This superiority is validated through rigorous theoretical analysis and extensive experimental evaluation. [ABSTRACT FROM AUTHOR]

This paper proposes a spherical evolution algorithm with two-stage search. Spherical search and hypercube search are combined to achieve individuals' evolution. A self-adaptive Gaussian scale factor and a variable scale factor are designed to adaptively control individuals' spherical and hypercube search area according to their search situations. Two search stages frequently switch with four search cases to enhance the balance between exploration and exploitation processes. A directed adjacency matrix is devised to analyze the relationship among individuals from the perspective of graph theory. Experiments compare the proposed algorithm with five algorithms with distinctive search patterns on twenty nine CEC2017 benchmark functions. The diversity analysis and graph theory analysis show the good population diversity and effective information spreading of the proposed algorithm. Twenty two real-world problems evaluate the practicality and optimization ability of the proposed algorithm. Finally, the computational time complexity demonstrates that the proposed algorithm is more efficient than the original spherical evolution algorithm. • A spherical evolution algorithm with two-stage search is proposed. • Spherical search and hypercube search are combined to adaptively guide individuals' evolution. • A directed adjacency matrix is firstly introduced to analyze the relationship among individuals. • Functions and real-world problems verify the superior search performance of the proposed algorithm. • The computational efficiency of the proposed algorithm is enhanced. [ABSTRACT FROM AUTHOR]

In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically selects promising individuals from population as guiders and proportionately allocates the individuals following them based on their fitness values and unsuccessful updates. Besides, we design a multi-segment mixed parameter setting to provide suitable parameters for individuals, considering feedback information from the population, individual requirements, and parameters' interaction. Additionally, we devise an adaptive population maintaining mechanism to refresh the population by replacing individuals with higher unsuccessful updates and removing those with poor fitness values. Unlike previous DE versions, our new algorithm dynamically and appropriately explores promising areas, refines parameter suitability, and accelerates convergence by updating and removing unpromising individuals. This enhances the algorithm search efficiency and achieves a good balance between exploration and exploitation. Finally, we evaluate the proposed algorithm's performance by comparing it with 17 typical or state-of-the-art algorithms on IEEE CEC2014 and CEC2017 test suites. Experimental results indicate that the proposed algorithm is a more promising optimizer. [ABSTRACT FROM AUTHOR]

As a population-based stochastic optimization algorithm, Gravitational Search Algorithm (GSA) has attracted numerous interests and has been applied in various applications. However, GSA has drawbacks such as uneven search and premature convergence in practical applications. This paper specifically explains the inherent characteristic of GSA in prioritizing the center position. Correspondingly, an improvement strategy of fitness normalization with mass shift is proposed, creating a situation where gravity and repulsion are mixed. Then, the global best mechanism with weights is incorporated into the particle's velocity update formula, which compensates for the difficulties in the later exploitation stage. Finally, an empirical formula for the initial gravitational constant related to the size of the solution space is proposed, which enhances the global search ability together with the former strategy. 12 shifted benchmark functions are used to construct 20 optimization problems ranging from 2 to 120 dimensions. The average performance of the proposed algorithm, other GSA and well-known algorithms are compared under the same budget. The results demonstrate that the proposed GSA not only effectively addresses the drawbacks of GSA and maintains good performance, but also exhibits strong competitiveness compared to various similar algorithms. [ABSTRACT FROM AUTHOR]