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In the paper Crucial and unsolved problems on Atanassov’s intuitionistic fuzzy sets, D.-F. Li pointed out that some kind of definitions of operations over Atanassov’s intuitionistic fuzzy sets are incorrect. We can see that, near 30 years after the first Atanassov’s papers, there exist some misunderstandings related not only on the name, but also on the basic operations on IFSs. Those misunderstandings concern, this time, on the operations of the sum and product. Li also casts doubt the equivalence of the intuitionistic fuzzy sets and the interval-valued fuzzy sets. In this paper, the Li’s reasoning is presented and commented. [ABSTRACT FROM AUTHOR]

Focusing on non-iterative approaches in training feed-forward neural networks, this special issue includes 12 papers to share the latest progress, current challenges, and potential applications of this topic. This editorial presents a background of the special issue and a brief introduction to the 12 contributions. [ABSTRACT FROM AUTHOR]

As one of the indirect causality, cascaded causality can be used to construct the event knowledge graph, causal inference, scenario analysis, etc. The existing GCN methods lack the mining of context information and relevant entity information, resulting in the poor ability of causality inference, which inevitably affects the extraction accuracy of cascade causality. To solve this problem, this paper proposes a causality extraction model based on a two-stage GCN to improve the extraction accuracy. To obtain rich features of entities, this work combines sentiment polarity and knowledge base to get the causality candidate entity library. Firstly, the BERT model is pre-trained using context information and relevant entity information extracted from the entity library to obtain the final entity nodes. Secondly, using the semantic dependency graph, each possible edge between any two entity nodes can be obtained, which are input into the first stage GCN to get a preliminary directed graph of causality. Finally, the directed graph of causality is input into the second stage GCN to achieve deep causality multi-hop inference. Thus, the cascade causality is inferred and extracted by the two-stage GCN model. Experiments show that the extraction accuracy of cascade causality has been further improved. [ABSTRACT FROM AUTHOR]

With the development of formal concept analysis, classical concept lattice cannot solve some problems in practice because of its strict conditions. Protoconcept, which is an extension of the formal concept, provides a new method of data processing. Three-way decisions is more in line with human cognition. Not like the decision of two ways, three-way cognition expands its scope of application. However, up to now, protoconcept is influenced by two-way decisions which leads to the restrictions of its applications. Hence, it is urgent to find a method to generalize protoconcept from the framework of two-way decisions. To give the solution to this urgent problem, the main contributions of this paper are the following: First, using the combination of protoconcept and three-way decisions, two models of three-way protoconcept are obtained. The algorithms for searching three-way protoconcept are built. Second, we investigate the constructions of three-way protoconcepts in double Boolean algebra. Third, the relationships between protoconcepts and three-way protoconcepts are found. Moreover, using examples that come from the real world, we explain the main results obtained above and show some applications. The two models for protoconcepts provided in this paper generalize the fields of thought from two-way decisions to three-way decisions. This follows that they are more in line with human thinking. Hence, the two models will certainly generalize the applications of protoconcept theory in the future. [ABSTRACT FROM AUTHOR]

An equity warrant allows warrant holders to buy stocks of listed companies at a certain price on a promissory day. Classified by the time of exercise right, there are two types of equity warrant, namely European equity warrants and American equity warrants. The European equity warrants allow the buyers to exercise their rights only on the expiring date. In contrast, American warrants enable the buyers to exercise their rights before or on the expiring date. Based on the assumption that the firm's value follows an uncertain exponential Ornstein–Uhlenbeck process instead of a stochastic process, this paper mainly solves the pricing problems of European and American equity warrants. The most significant advantage of this paper is offering the corresponding formulas to calculate equity warrants, which are not complicated compared with the classical stochastic financial theory. Besides, the minimum cover estimation method is applied to estimate the parameters in an uncertain exponential Ornstein–Uhlenbeck equation, and a simulation example is provided to show the effectiveness of the proposed pricing formulae. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low learning efficiency and slow convergence speed when mobile robot uses reinforcement learning method for path planning in complex environment, a reinforcement learning method based on each round path planning result is proposed. Firstly, the algorithm adds obstacle learning matrix to improve the success rate of path planning; and introduces heuristic reward to speed up the learning process by reducing the search space; then proposes a method of dynamically adjusting the exploration factor to balance the exploration and utilization in path planning, so as to further improve the performance of the algorithm. Finally, the simulation experiment in grid environment shows that compared with Q-learning algorithm, the improved algorithm not only shortens the average path length of the robot to reach the target position, but also speeds up the learning efficiency of the algorithm, so that the robot can find the optimal path more quickly. The code of EPRQL algorithm proposed in this paper has been published to GitHub: https://github.com/panpanpanguoguoqian/mypaper1.git. [ABSTRACT FROM AUTHOR]

Due to the heterogeneity of program language and natural language query, it is difficult to identify the semantic relationship between them, which leads to the low efficiency of code recommendation. In order to solve the problems of the above code recommendation technology, a code recommendation method based on joint embedded attention network (JEAN) is proposed in this paper. The method uses GRU Network to embed code snippets and describe queries into vector representation, which solves the problem of heterogeneous code snippets and natural language queries. The Attention mechanism is then used to distribute totally different weights to different components of every mode of the code snippet. The reason for the Attention mechanism is that different components of every mode of the code snippet contribute differently to the semantic vector of the final code snippet, making it interpretable. Finally, two commonly used evaluation indexes of information retrieval, SuccessRate@k and MRR, are used for experimental comparison with other baseline models. The experimental results show that the code recommendation method based on joint embedded attention network proposed in this paper can effectively recommend appropriate code snippets according to the needs of developers, and its performance is better than other baseline methods. [ABSTRACT FROM AUTHOR]

Since frequent traffic accidents bring great losses to people's safety and social property, this paper takes the results of traffic accident risk prediction as the basis so that it can help city managers to reasonably deploy police force, relieve traffic pressure, avoid traffic accidents and provide safe guidance to pedestrians. Based on this paper, a deep learning framework including spatiotemporal attention mechanism is proposed to solve the problem of traffic accident prediction in urban areas, and the experimental simulation shows that the accuracy of traffic accident risk prediction proposed in this paper reaches 94%. [ABSTRACT FROM AUTHOR]

The probabilistic linguistic term set (PLTS) shows great superiority in expressing decision-makers' opinions. The multi-attribute decision-making (MADM) problem under a PLTS environment has gained attention from numerous scholars. However, the majority of current studies are not precise enough in capturing information on PLTS. To address this problem, this paper presents a preference ranking organization method for enrichment of evaluations (PROMETHEE) based on the redefined PLTS and novel score function to solve MADM problems under a PLTS environment. First, an asymmetric normalized PLTS based on prospect theory (ANPLTSPT) is developed. Compared with the PLTS, ANPLTSPT offers a more realistic portrayal of decision-makers' psychological state while ensuring the superiority of the PLTS. Second, regarding the structural complexity of ANPLTSPT, this paper attempts to simplify the computational process through a score function that can embody the characteristics of ANPLTSPT. Inspired by previously formulated score functions, a novel score function called Score-InInHe is developed, the corresponding definitions are given, and some further properties are discussed. With the support of the proposed Score-InInHe, the total score entropy is defined and an objective method to determine the attribute weights is proposed. Finally, the proposed approach is applied to the selection of a green supplier and the determination of air quality. The validity and realistic applicability of the proposed approach are demonstrated through comparative analyses and discussions. [ABSTRACT FROM AUTHOR]

The identification of apple leaf diseases is crucial to reduce yield reduction and timely take disease control measures. Employing deep learning for apple leaf disease identification is challenging because of the limited availability of samples for supervised training and the serious class imbalance. Hence, this paper proposes an accurate deep learning-based pipeline to solve the problem of limited data sets on farms and reduce the partiality due to serious class imbalance. Firstly, an improved cycle-consistent adversarial networks (CycleGAN) is used to generate synthetic samples to improve the learning of data distribution and solve the problems of small data sets and class imbalance. Secondly, ResNet is trained as a baseline convolutional neural network classifier to classify apple leaf diseases. The experimental results show that ResNet has the highest recognition accuracy on the test set, reaching 97.78%, and the classification accuracy is significantly improved by the generated synthetic samples (+ 14.7%). In addition, the experiment results of t-distributed stochastic neighbor embedding (t-SNE) and visual Turing test visually confirmed that the images generated by improved CycleGAN have much better quality and are more convincing. [ABSTRACT FROM AUTHOR]

In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist's natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem. [ABSTRACT FROM AUTHOR]

In this paper, ant colony optimization (ACO) and grey wolf optimization (GWO) algorithms are used for solving the joint user and receive antenna scheduling problem of multi-user multiple input multiple-output systems in the uplink channel. A subset of users among all the available users should be allowed to send their data to the appropriate receive antenna of the base station (BS). The search space for assigning appropriate user with the appropriate receive antenna of the BS is very large. Searching through this large search space is quite time-consuming process which cannot be accomplished within the real-time frame. To overcome this, ACO and GWO soft computing techniques have been implemented for this problem. In this paper, it is shown that both ACO and GWO accomplish this huge task with very low computation complexity. Simulation results presented in this paper are verifying the effectiveness of ACO and GWO for solving such complex problems. Moreover, it has also been showcased that GWO performs better than ACO and binary particle swarm optimization (BPSO) techniques. Both GWO and ACO attains the system sum-rate very close to that of exhaustive search algorithm. Furthermore, different statistical parameters for these soft computing techniques like BPSO, ACO, and GWO have been presented and compared to assess the efficacy of these meta-heuristic methods. [ABSTRACT FROM AUTHOR]

Topic detection is an information processing technology designed to help people deal with the growing problem of data information on the Internet. In the research literature, topic detection methods are used for topic classification through word embedding, supervised-based and unsupervised-based approaches. However, most methods for topic detection only address the problem of clustering and do not focus on the problem of topic detection accuracy reduction due to the cohesiveness of topics. Also, the sequence of biterm during topic detection can cause substantial deviations in the detected topic content. To solve the above problems, this paper proposes a topic detection method based on KM-LSH fusion algorithm and improved BTM model. KM-LSH fusion algorithm is a fusion algorithm that combines K-means clustering and LSH refinement clustering. The proposed method can solve the problem of cohesiveness of topic detection, and the improved BTM model can solve the influence of the sequence of biterm on topic detection. First, the text vector is constructed by processing the collected set of microblog texts using text preprocessing methods. Secondly, the KM-LSH fusion algorithm is used to calculate text similarity and perform topic clustering and refinement. Finally, the improved BTM model is used to model the texts, which is combined with the word position and the improved TF-IDF weight calculation algorithm to adjust the microblogging texts in clustering. The experiment results indicate that the proposed KM-LSH-IBTM method improves the evaluation indexes compared with the other three topic detection methods. In conclusion, the proposed KM-LSH-IBTM method promotes the processing capability of topic detection in terms of cohesiveness and the sequence of biterm. [ABSTRACT FROM AUTHOR]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a classical computational method that optimizes a problem by iteratively trying to find the optimal solution. It still suffers somes defects such as poor local search ability, low search accuracy and premature convergence, especially in high-dimensional complex problems. In order to address these issues, this paper has proposed a novel PSO algorithm with distributed delays of adaptive weights and evolved acceleration coefficients (PSO-DWC). The main idea of the proposed improved PSO algorithm is three-fold: (1) a mechanism is introduced to evaluate the current evolutionary state by evolutionary factors of the swarm and to predict the next state by a probability transition matrix; (2) distributed time-varying time-delays are added into the velocity updated model; (3) adaptive inertia weight varies according to evolutionary factors, which describes the population distribution information; and newly-introduced evolved acceleration coefficients are determined by the predict next evolutionary state of the swarm. Owing to the promising issues mentioned above, the PSO-DWC algorithm has the advantages of keeping the diversity of particles, balancing the local and global search abilities and reaching to an acceptable solution. Experiments on twenty well-known benchmark functions have demonstrated that the proposed PSO-DWC algorithm has a superior performance over other five well-known PSO algorithms in high dimensional search space. Statistical significance tests verify the superiority of the new algorithm. Therefore it can be concluded that the novel PSO-DWC algorithm is able to solve the optimization problems with powerful global search and efficient convergence. [ABSTRACT FROM AUTHOR]

The operations proposed by Dombi based on t-norn (TN) and t-corom (TCN) are generally known as Dombi operations, which offer versatility in the working behavior of parameters. Over the last decade, group decision-making has been a very active research field. Especially, the development of new operational rules, aggregation operators, and multi-attribute group decision-making techniques based on these rules and operators have recently piqued the interest of scientists. Acknowledging the importance of t-spherical fuzzy sets and linguistic variable in this paper, firstly, we define the notion of linguistic T-spherical fuzzy set (Lt-SFS) where membership degree, abstinence degree and non-membership degree are presented in the form of linguistic variables. Dombi operations, score function, accuracy function for linguistic T-spherical fuzzy numbers (Lt-SFNs) are defined and some prominent properties of Dombi operations are then investigated. Furthermore, two aggregations operators based on Dombi operations namely, linguistic t-spherical fuzzy Dombi weighted averaging operator and linguistic t-spherical fuzzy Dombi weighted geometric operator are also developed. At that point, these Dombi operators are used to establish an extension of the technique for order of preference by similarity to ideal solution (TOPSIS) method, and a multi-attribute group decision-making approach is proposed to solve decision-making problems which is the key innovation of this paper. Finally, we apply the proposed technique for the selection of the best variety of maize and a comparison analysis is provided to demonstrate its applicability and feasibility. [ABSTRACT FROM AUTHOR]

The capsule network (CapsNet) is a promising model in computer vision. It has achieved excellent results on MNIST, but it is still slightly insufficient in real images. Deepening capsule architectures is an effective way to improve performance, but the computational cost hinders their development. To overcome parameter growth and build an efficient architecture, this paper proposes a tensor capsule layer based on multistage separable convolutions and a dense capsule architecture. Multistage separable convolutions can effectively reduce the parameters at the cost of a small performance loss. In the dense capsule architecture, the use of dense connections allows the capsule network to be deeper and easier to train. Combining these two can achieve a novel lightweight dense capsule network. Experiments show that this network uses only 0.05% of the parameters of the CapsNet, but the performance is improved by 8.25% on CIFAR10. In addition, the full tensor capsule method is proposed to solve the problem of capsule network parameters changing with image scale. Experiments prove that this method can keep the parameters unchanged while affecting the performance in a small amount. In order to lighten the fully connected capsule layer, a dynamic routing based on separable matrices is proposed. In addition to applying it to our models, this algorithm also compresses the CapsNet by 41.25% while losing only 0.47% performance on CIFAR10. The parameter utilization index is proposed to quantify the relationship between parameters and performance. To our knowledge, this is the first paper to study lightweight capsule network. [ABSTRACT FROM AUTHOR]

How to handle conflict in Dempster-Shafer evidence theory is an open issue. Many approaches have been proposed to solve this problem. The existing approaches can be divided into two kinds. The first is to improve the combination rule, and the second is to modify the data model. A typical method to improve combination rule is to assign the conflict to the total ignorance set Θ . However, it does not make full use of conflict information. A novel combination rule is proposed in this paper, which assigns the conflicting mass to the power set (ACTP). Compared with modifying data model, the advantage of the proposed method is the sequential fusion, which greatly decrease computational complexity. To demonstrate the efficacy of the proposed method, some numerical examples are given. Due to the less information loss, the proposed method is better than other methods in terms of identifying the correct evidence, the speed of convergence and computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

The alternating direction method of multipliers (ADMM) is an effective algorithm for solving optimization problems with separable structures. Recently, inertial technique has been widely used in various algorithms to accelerate its convergence speed and enhance the numerical performance. There are a lot of convergence analyses for solving the convex optimization problems by combining inertial technique with ADMM, while the research on the nonconvex cases is still in its infancy. In this paper, we propose an algorithm framework of inertial regularized ADMM (iRADMM) for a class of two-block nonconvex optimization problems. Under some assumptions, we establish the subsequential and global convergence of the proposed method. Furthermore, we apply the iRADMM to solve the signal recovery, image reconstruction and SCAD penalty problem. The numerical results demonstrate the efficiency of the iRADMM algorithm and also illustrate the effectiveness of the introduced inertial term. [ABSTRACT FROM AUTHOR]

The minimum weighted vertex cover (MWVC) problem is to find a subset of vertices that can cover all the edges of the network and minimize the sum of the vertex weights in the vertex subset. MWVC is a generalization of the minimum vertex cover (MVC) problem and can be regarded as a combinatorial optimization problem. This paper proposes an evolutionary algorithm based on the snowdrift game to solve the MWVC problem. We use the evolutionary algorithm framework, and the snowdrift game is combined to construct and improve the initial solution. First, the network's vertex is regarded as an intelligent agent, and each vertex and its neighbors play a snowdrift game to form an initial solution. Then local search and global search are used to improve the initial solution. The solution is improved according to the proposed score function in the local search stage to obtain a better MWVC solution. The evolutionary algorithm is used in the global search stage to escape the local optimum. Experiments on different networks show that the proposed algorithm are effective on weighted networks. [ABSTRACT FROM AUTHOR]

The probability transformation of mass functions plays a crucial role for decision making in Dempster–Shafer evidence theory. From the perspective of information, the existing transformation methods just consider the information of original mass functions from the obverse side, and then use mathematical tools for information processing. However, the limited amount of information has a restriction on decision performance. To solve this problem, in this paper we provide a new insight for probability transformation. The negation operation is used to obtain additional information of mass functions from the opposite side. By the joint use of mass functions information from the obverse and opposite sides, the informative belief functions of different elements are obtained, then the conditional belief function is applied to calculate proportions of singletons in multi-subset elements, and finally mass functions can be transformed to probabilities by proportion reasonably. Experimental results and related analysis are provided to show the rationality of the new transformation method. [ABSTRACT FROM AUTHOR]

This paper addresses the two-dimensional irregular bin packing problem, whose main aim is to allocate a given set of irregular pieces to larger rectangular containers (bins), while minimizing the number of bins required to contain all pieces. To solve the problem under study a dynamic hierarchical hyper-heuristic approach is proposed. The main idea of the hyper-heuristics is to search the space of low-level heuristics for solving computationally difficult problems. The proposed approach is "dynamic" since the low-level heuristic to be executed is chosen on the basis of the main characteristics of the instance to be solved. The term "hierarchical" is used to indicate the fact that the main hyper-heuristic can execute either simple heuristics or can run in a "recursive fashion" a hyper-heuristic. The developed solution strategy is evaluated empirically by performing extensive experiments on irregular packing benchmark instances. A comparison with the state-of-the-art approaches is also carried out. The computational results are very encouraging. [ABSTRACT FROM AUTHOR]

This paper addresses the electricity pricing problem with demand-side flexibility. The interaction between an aggregator and the prosumers within a coalition is modeled by a Stackelberg game and formulated as a mathematical bi-level program where the aggregator and the prosumer, respectively, play the role of upper and lower decision makers with conflicting goals. The aggregator establishes the pricing scheme by optimizing the supply strategy with the aim of maximizing the profit, prosumers react to the price signals by scheduling the flexible loads and managing the home energy system to minimize the electricity bill. The problem is solved by a heuristic approach which exploits the specific model structure. Some numerical experiments have been carried out on a real test case. The results provide the stakeholders with informative managerial insights underlining the prominent roles of aggregator and prosumers. [ABSTRACT FROM AUTHOR]

The graph coloring problem is an NP-hard problem. Currently, one of the most effective methods to solve this problem is a hybrid evolutionary algorithm. This paper proposes a hybrid evolutionary algorithm NERS_HEAD with a new elite replacement strategy. In NERS_HEAD, a method to detect the local optimal state is proposed so that the evolutionary process can jump out of the local optimal state by introducing diversity on time; a new elite structure and a replacement strategy are designed to increase the diversity of the evolutionary population so that the evolution process can not only converge quickly but also jump out of the local optimal state in time. The comparison experiments with current excellent graph coloring algorithms on 59 DIMACS benchmark instances show that NERS_HEAD can effectively improve the efficiency and success rate of solving graph coloring problems. [ABSTRACT FROM AUTHOR]

A memetic algorithm based on MOEA/D is presented to deal with the uncapacitated multiobjective examination timetabling problem in this paper. The examination timetabling problem is considered as a two-objective optimization problem in this paper, while it is modeled as a single-objective optimization problem generally. The framework of a multiobjective evolutionary algorithm with decomposition (MOEA/D) is first employed to guide the evolutionary process. Two special local search operators are designed to find better individuals. The proposed algorithm is tested on 11 benchmark examination timetabling instances. Experimental results prove that the proposed algorithm can produce a promising set of nondominated solutions for each examination timetabling instance. [ABSTRACT FROM AUTHOR]

Generalized hesitant fuzzy numbers (GHFNs) are able to directly manage situations in which we may encounter a finite set of known values with a finite set of degrees of doubt as quantitative approximations of an uncertain situation/quantification of a linguistic expression. They are new extensions of hesitant fuzzy sets, which have been considered in this paper. In fact, in this paper, GHFNs will be utilized to model the uncertainty of the assessment values of options against criteria in multi-attribute decision making (MADM) problems. It means that all of the elements of decision matrix are GHFNs. Then, the technique for order of preference by similarity to ideal solution (TOPSIS) method, as a very successful method in solving MADM problems, will be updated to be used with GHFNs. To this end, the distance between GHFNs must be defined to obtain the distances between given alternatives from each of two subjective alternatives (positive/negative ideal solutions). Thus, three existing famous distance measures, i.e., general distance ( d g ), Hamming distance ( d h ), and Euclidean distance ( d e ) measures, have been updated for GHFNs firstly. Then, the new TOPSIS method will be proposed based on GHFNs. Finally, the numerical examples have been appointed to illustrate the proposed method, analyze comparatively and validate it. [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent's opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems. Source codes of GMO are publicly available at https://github.com/farshad-rezaei1/GMO. [ABSTRACT FROM AUTHOR]

Time-dependent reliability is a key index for reliability evaluation and life prediction of existing bridges, which can reflect the service status of bridges. Therefore, the time-dependent reliability of the bridge must be calculated and analyzed in time to ensure the safety of the bridge. Many scholars have established different time-dependent reliability models, but the model contains complex multiple integrals and is difficult to calculate. In this case, the current method for calculating time-dependent reliability is Monte Carlo method, which is inefficient and cannot meet the engineering needs. In order to solve the problem that time-dependent reliability is difficult to solve, this manuscript proposes a method for solving time-dependent reliability of bridges using neural networks, which is suitable for existing time-dependent reliability models. Then, compared with the results of Monte Carlo method, the results show that the neural network used in this paper is accurate. At the same time, it can also significantly improve the computational efficiency and has significant advantages compared with the existing calculation methods. [ABSTRACT FROM AUTHOR]

Multi-criteria group decision-making (MCGDM) problems, where correlations commonly exist among input arguments, are becoming increasingly complex. However, most of the existing consensus-reaching methods for MCGDM problems fail to adequately consider the effects of these interactions among criteria and experts, which may bring about inaccurate results. Therefore, this paper establishes a novel MCGDM framework based on the generalized Shapley value to solve the consensus-reaching problem with interval-valued Pythagorean fuzzy sets (IVPFS). First, experts' evaluations are collected using IVPFS, which offers a more flexible way to express this vague information. Second, the interval-valued Pythagorean fuzzy Choquet integral operator and the interval-valued Pythagorean fuzzy Shapley aggregation operator are developed to fuse the decision information with complementary, redundant, or independent characteristics. Third, an integrated consensus-reaching algorithm is established to improve group consensus by iteratively updating the evaluations until the group consensus level reaches the preset threshold. Then, the classical PROMETHEE method is extended using the generalized Shapley value within an IVPFS context to derive a more scientific ranking result. Finally, a case study for a sustainable supplier evaluation problem is presented to validate the proposed method. The results and comparative analysis show that the proposed method can represent experts' evaluations more flexibly, integrate inputs with interrelationships more effectively, and improve group consensus more efficiently. [ABSTRACT FROM AUTHOR]

Inverse data envelopment analysis technique is a useful tool in planning and management control to estimate the level of input/output of decision-making units to achieve the predetermined efficiency targets. In the real problems, data are time-dependent that also this technique is influenced by this parameter. The current paper studies this technique in a dynamic framework. In fact, the problem of estimating the input and output levels simultaneously is studied when data are time-dependent. This problem is solved using multiple-objective programming tools in the framework of dynamic inverse data envelopment analysis. The proposed models in the current study, unlike other existing models in the literature of inverse data envelopment analysis, can solve the problem of estimating the input and output levels such that the unit maintains or improves its current efficiency level to a pre-defined amount in the assessment window. Necessary and sufficient conditions are derived for the estimation of input and output levels through the linear multiple-objective programming problems. Also, the applicability and limitations of the provided method are explained via a real example of the banking sector on a period of six months. The proposed approach is confirmed according to the obtained numerical results as well. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent attendance monitoring system based on spatio-temporal human action recognition, which includes human skeleton gait recognition, multi-action body silhouette recognition and face recognition. Our system solves several problems, for example, when a mask is worn to conceal the face, which leads to a decrease in recognition accuracy performance, and when a 3D face mask is used to fake an identity. The skeleton gait feature of our intelligent attendance monitoring system uses a temporal weighted K-nearest neighbours algorithm to train the recognition model and carry out identification, while the multi-action body silhouette feature uses a multiple K-nearest neighbours algorithm to train the recognition model, identify the person and vote on the outcome. Using the proposed system, which integrates skeleton gait features, action silhouette features and face features, more effective recognition can be achieved. When the system encounters a situation with feature masking, such as when an individual is wearing a mask or has changed their clothes, or when the viewing angle is masked, it can continue to deliver good recognition ability through multi-angle skeleton synthesis gait recognition. Our experimental results show that the recognition accuracy of the system is 83.33% when a specific person wears a mask and passes through a monitored area. The intelligent attendance monitoring system uses a LINE messaging API as the access control notification function and provides a responsive web platform that allows managers to perform follow-up management and monitoring. [ABSTRACT FROM AUTHOR]

Secret communication of sensitive data must progress in a trustworthy environment through data hiding. Using Mamdani fuzzy logic to identify color proximity at the block level and a shared secret key and post-processing system, this paper attempts to develop a robust data hiding scheme with similarity measures to ensure good visual quality, robustness, imperceptibility and enhance the security. In accordance with the Gestalt principle, proximity among the nearby objects is higher, whose value varies from expert to expert. Therefore, a possibility for type-I fuzzy logic to be used to evaluate proximity. Fuzzy proximity is computed by means of a difference in intensity (colordiff) and distance (closeness). Further, the block color proximity obtained from the proximity calculation network is graded using an interval threshold. Accordingly, data embedding is processed in the sequence generated by the shared secret keys. The tampering coincidence problem is solved through a post-processing approach to increase the quality and accuracy of the recovered secret message. The experimental analysis, steganalysis and comparisons clearly illustrate the effectiveness of the proposed scheme in terms of visual quality, structural similarity, recoverability and robustness. [ABSTRACT FROM AUTHOR]

The trisecting–acting–outcome model of a three-way decision is a practical approach to solving complex problems, consisting of three components: trisecting, acting, and outcome. Trisecting means dividing a complex problem into three parts based on an evaluation function, and acting means designing the corresponding action to apply to these three parts. The outcome denotes evaluation effectiveness based on the first two steps. Measuring this outcome is a challenging and moving question after trisecting-and-acting (T &A). This paper proposes a method to measure outcome based on object changes before and after T &A based on top-down and bottom-up perspectives, depending on the decision needs. First, evaluation functions are constructed that extend the traditional evaluation functions to measure changes. Then, based on the introduced evaluation functions, qualitative and quantitative of three-way changes are introduced and analyzed. Furthermore, we present the processes that determine the optimal strategy with the quantitative analysis. The analysis of several examples shows the effectiveness and practicality of our proposed method. [ABSTRACT FROM AUTHOR]

Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. [ABSTRACT FROM AUTHOR]

The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa–Istanbul–Pendik–Trieste (Ro-Ro)–Austria–Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary approach-based solution of multi-objective transportation-p-facility location problem (MOT-p-FLP) that minimizes overall transportation time, cost of transportation, and carbon emission (CE) from available sites to facility sites by seeking transported product quantities and the facility locations in the Euclidean plane. Genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), and modified Self-Adaptive Multi-Population Elitism Jaya Algorithm (SAMPE JA) are utilized to solve the problem. We compared obtained compromise solutions of the problem by evolutionary algorithms with population size, the maximum number of generations, crossover probability, mutation probability, and computational time. Sensitivity analysis for supply, demand, and carbon cap parameters is incorporated in the model's solution, which helps the decision maker make the appropriate decision. These evolutionary algorithms (NSGA-II and NSGA-III) give Pareto-optimal solutions, and it helps management decide on the selection of p-facility locations. They could transport their product to the facility locations easily, with minimum transport, CE costs, and transportation time. As a result, management can give equal attention to their profit and environment, which helps to build world market credibility. At last, the paper concludes the results. [ABSTRACT FROM AUTHOR]

Currently, the high dropout rate of massive open online course (MOOC) has seriously affected its popularity and promotion. How to effectively predict the dropout status of students in MOOC so as to intervene as early as possible has become a hot topic. As we know, different students in MOOC have big differences in learning behaviors, learning habits, and learning time, etc. This leads to different student samples having different effects on the prediction performance of the machine learning-based dropout prediction model (DPM). This is because the performance of machine learning-based classifiers heavily depends on the quality of training samples. To solve this problem, in this paper, a new DPM based on machine learning is proposed. Since the traditional neighborhood concept has nothing to do with the label of the sample, a new neighborhood definition, i.e., the max neighborhood, is first given. It is not only related to the distance between samples, but also related to the labels of the samples. Then, the calculation and realization algorithm of the initial weight of each student sample is studied based on the definition of the max neighborhood, which is different from the commonly methods of randomly selecting initial values. Next, the optimization method of the initial weight of the student sample is further studied using the intelligent optimization method. Finally, the classifiers trained by the weighted training samples are used as DPM. Experimental results of direct observation and statistical testing on public data sets indicate that the training sample weighting and intelligent optimization technology can significantly improve the predictive performance of DPM. [ABSTRACT FROM AUTHOR]

In this research paper, we will solve problems of Bi-level linear fractional programming (BL-LFP) by proposing an interactive approach. Based on the imposition of the relationship DM. to obtained adequate solution, DMs will updating the minimal adequate level at upper level permanently, and that is through. Firstly, the decision makers uncertainty is described by introducing the membership function and non-membership function. Secondly, the opting of minimum adequate degree, leads to obtain the adequate solution, with the overall adequate balance considerations among two levels. For more, this paper gives an algorithm of the proposed approach. At the end, we give a numerical example to explain the feasibility of that approach. [ABSTRACT FROM AUTHOR]

The heuristic tuning method of fractional-order proportional–integral–derivative (FOPID) control systems lacks robustness, and its performance often changes with specific controlled plants. To solve this problem, this paper proposes a new modified aquila optimizer for tuning the parameters of the FOPID controller, which has strong plant adaptability and can be applied to a large class of different controlled plants. A series of new operational mechanisms, including Tent map-based initialization, probability-based dynamic update, greedy-based Gauss mutation, a fully random search strategy with uniform distribution, and the wraparound dynamic weight update for local exploitation, are proposed to tackle the existing problems of the classical aquila optimizer, such as slow convergence, low precision, and local optimum. Standard benchmark functions are used to test the proposed modified aquila optimizer, showing superior performance in terms of convergence speed, precision, and robustness. The Wilcoxon and Friedman tests statistically proved the significant difference of the modified aquila optimizer from other competitors. Five control system cases with different plants further validate the effectiveness, feasibility, wide adaptability, and superiority of the proposed modified aquila optimizer for regulating FOPID controller parameters. It is approved that the proposed modified aquila optimizer with new mechanisms has wide plant adaptability, deeming a good prospect for the tuning of optimal FOPID controller. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for solving the system of fuzzy relation equations (SFRE) with extended max–min composition for inverse inference problems. The properties of interval and constrained solutions with granular and relational structure of the solution set are investigated. The extended max–min SFRE can be represented in the form of the max–min subsystems aggregated using the min operator or dual min–max subsystems aggregated using the max operator. When decomposing the SFRE, the set of solutions can be decomposed into the lower and upper subsets bounded by the same aggregating solutions. Each lower (upper) subset is defined by the unique greatest (least) or aggregating solution and the set of minimal (maximal) solutions. Following Bartl et al. (Fuzzy Sets Syst 187:103–112, 2012), to avoid excessive granularity and ensure interpretability of the interval solutions when restoring causes through observed effects, the constraints in the form of linguistic modifiers are imposed on the measures of causes significances. The interval solutions are modeled by the complete crisp solutions, that is, the maximum solutions for the vectors of binary weights of the linguistic modifiers. The search for approximate solutions of the SFRE amounts to solving the optimization problem using the genetic algorithm. Due to the properties of the solution set, the genetic search for the lower and upper subsets is parallelized for each aggregating solution. The developed method makes it possible to simplify the search for the solution set based on the constraints on accuracy (interpretability) of the applied problem. [ABSTRACT FROM AUTHOR]

Decision making is a universal behavior based on human cognitive information. In the real world, information related to human cognition is characterized by uncertainty and partial reliability, which is challenging to express with traditional and precise concepts. To better describe this type of uncertain information, Z-number is introduced by Zadeh, which contains uncertain information with both probability and fuzziness. Recently, Yager proposed the fusion of multiple multi-criteria aggregate functions, especially the fusion of multiple OWA-type aggregate functions, to deal with the group decision problem. However, such aggregation functions exhibit limitations in facing the uncertainty of expert information in real-world decision-making scenarios. To overcome this shortcoming, this paper extends Yager's aggregation method to the Z-number field and further propose a new group decision-making method based on the OWA aggregation operator and Z-number. The maximum entropy optimization model based on a genetic algorithm is used to determine the hidden probability distribution in Z-numbers to aggregate the Z-numbers, which solves the problem of information loss caused by ignoring the hidden probability distribution in the existing Z-number aggregation methods, and greatly preserves the original meaning of Z-number. Some numerical examples are used to demonstrate the rationality and effectiveness of the proposed method. Finally, a comparative analysis with existing methods expounds on the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A fuzzy equivalence relation can be used for clustering. But when using it in applications, we often get a similarity relation rather than an equivalence one because of various reasons. We need to reform it into an equivalence relation close to it to cluster. A commonly used method is transitive closure method, but it usually results in serious distortions about the relation. This paper further studies fuzzy similarity and equivalence relations using fuzzy graphs, and obtains some new results. The defects of transitive closure method are analyzed, and an improved clustering algorithm is given, but it cannot eliminate the inconsistency phenomenon in classification hierarchy structure. To solve this problem, the optimal fuzzy equivalence relation of similarity relation is studied. An optimization model which can derive it exactly is given, but it is too complex for applications. An effective approximation algorithm to get the optimal equivalence relation is thus presented. Several examples and some discussions are also given to illustrate the given methods. [ABSTRACT FROM AUTHOR]

In this paper, new concepts to use clique covering of a fuzzy graph are introduced for optimization of parameters involved in business strategy. For this purpose, four algorithms are designed for finding necessary parameters and sets of a fuzzy graph which is helpful for constructing a cordon of linear programming problems. The linear programming problems are constructed with suitable optimization functions and constraints. The strengths of the cliques present in fuzzy graph get a new look in this paper. Facility location problems are characterized and solved with a new strategy optimization problems by using concept of clique covering of fuzzy graphs for a smooth business strategy to have a maximized total gain. This optimization process will help for developing a part of sustainable economic growth all over the world. Some new definitions are given with relevant examples of fuzzy graphs. An illustration is given to elaborate all mathematical terminologies. Also, a real-life application to optimize different parameters in a business network by solving the programming problems with the help of the mathematical software "LINGO" keeping the fuzziness of the parameters involved in the considered fuzzy graph. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEAs) have been successfully employed to solve many scientific and engineering problems. However, many algorithms perform ill in maintaining diversity and convergence simultaneously. In this paper, we devised a novel operator selection framework based on two collaborative indicators, generational distance (GD) and maximum spread (MS) to improve the diversity while maintaining a good convergence. By calculating the variation of GDs and MSs over the past 7 iterations, an instruction is conveyed to select a proper operator to execute next 7 iterations. This process is repeated until it reaches the maximum iteration. Two operators are embedded in this algorithm which are differential evolution operator (DE/rand/1) and our proposed crow search operator which is deemed to be efficient in explorating the search space. MOEA/D is utilized as the basis framework of our proposed algorithm. Experiments indicate that our proposed algorithm is valid and outperforms other famous algorithms in terms of diversity and convergence. In the end, a particular aeroengine maintenance scheduling problem is solved by our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers a passive target localization problem based on the noisy time of arrival measurements obtained from multiple receivers and a single transmitter. The maximum likelihood (ML) estimator for this localization problem is formulated as a highly nonlinear and non-convex optimization problem, where conventional optimization methods are not suitable for solving such a problem. Consequently, this paper proposes an improved adaptive hybrid firefly differential evolution (AHFADE) algorithm, based on hybridization of firefly algorithm (FA) and differential evolution (DE) algorithm to estimate the unknown position of the target. The proposed AHFADE algorithm dynamically adjusts the control parameters, thus maintaining high population diversity during the evolution process. This paper aims to improve the accuracy of the global optimal solution by incorporating evolutionary operators of the DE in different searching stages of the FA. In this regard, an adaptive parameter is employed to select an appropriate mutation operator for achieving a proper balance between global exploration and local exploitation. In order to efficiently solve the ML estimation problem, this paper proposes the well-known semidefinite programming (SDP) method to convert the non-convex problem into a convex one. The simulation results obtained from the proposed AHFADE algorithm and well-known algorithms, such as SDP, DE and FA, are compared against Cramér–Rao lower bound (CRLB). The statistical analysis has been performed to compare the performance of the proposed AHFADE algorithm with several well-known algorithms on CEC2014 benchmark problems. The obtained simulation results show that the proposed AHFADE algorithm is more robust in high-noise environments compared to existing algorithms. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models. [ABSTRACT FROM AUTHOR]