This paper proposes an evolutionary contribution and problem heuristic information ensemble-based computing resource allocation scheme for cooperative co-evolutionary algorithms. For problem heuristic information, this paper assembles the correlation sensitivity of variables in each subproblem and the dimension ratio of this subproblem; for evolutionary contribution, this paper assembles the historical and the current evolutionary contributions of each subproblem. By assembling these two crucial factors, the devised method computes the selection probability of each subproblem and then randomly picks one subproblem by the roulette wheel selection strategy to undergo optimization in each iteration. In this way, computing resources are preferentially allocated to those subproblems with high complexity manifested by the problem heuristic information and high fitness improvement reflected by the evolutionary contribution. With this method, cooperative co-evolutionary algorithms expectedly fully utilize the computing resources to achieve satisfactory performance in addressing large-scale optimization problems. By combining the devised method with 6 latest decomposition methods along with two evolutionary optimizers, this paper has conducted experiments to compare it with 7 state-of-the-art computing resource allocation methods on two popular suites of large-scale optimization problems. Experimental results have proved that the devised method outperforms the 7 compared methods in helping cooperative co-evolutionary algorithms achieve better performance.

Grasping the intrinsic properties of protein structure is crucial for comprehending relevant biological mechanisms, with protein flexibility standing out as a critical aspect. Therefore, the prediction of protein flexibility is of great importance in understanding molecular mechanisms. We propose a deep learning method named ResaPred, which extracts diverse features from protein sequences, such as secondary structure, torsion angle, solvent accessibility, etc. ResaPred is a novel deep network based on a modified 1D residual module and a self-attention mechanism, which effectively extracts deep key features related to flexibility. The modified 1D residual module consists of three convolution layers, with batchnorm and relu layers added after each layer to prevent gradient explosion or vanishing. Incorporating self-attention mechanisms into neural network architectures introduces a significant advantage in capturing long-range dependencies within sequential data. We conduct experiments on the non-strict and strict cases, and achieve state-of-the-art results in predicting flexibility compared to existing methods. Furthermore, we extended our analysis to explore the correlation between protein secondary structure and solvent accessibility with flexibility. Finally, we used two important viral proteins as case studies, confirming the effectiveness of our method in recognizing the flexibility of protein structures.

The interaction between non-coding RNA (ncRNA) and protein (ncRPI) plays a crucial role in many physiological activities and disease progression. To identify ncRPIs on a large scale by computational methods based on deep learning is a common practice. However, existing computational methods face challenges such as low feature expression and redundant suppression capability when processing high dimensional feature data. To this end, we propose a new prediction model, called FSKansformer, in which a feature salience module is introduced to highlight useful information and suppress noise of multi-view feature matrices. In order to reduce the loss of feature information caused by serial extraction of global feature and local feature, we propose an parallel extraction framework in which a improved Kansformer is designed to extract global high-dimensional features, BiLSTM and LSTM techniques are used to extract local high-dimensional features simultaneously. Finally, the fused global-local high-dimensional features are input into the three-layer KAN network for dimensionality reduction to generate the final prediction score. Experiment results show that FSKansformer achieves state-of-the-art performance on five benchmark datasets compared with other models.

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.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.

RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation and site-directed mutagenesis. In this work, a new parallel network that integrates the multi-head attention mechanism and the expectation pooling is proposed, named MAHyNet. The left-branch network of MAHyNet hybrids convolutional neural networks (CNNs) and gated recurrent neural network (GRU) to extract the features of one-hot. The right-branch network is a two-layer CNN network to analyze physicochemical properties of RNA base. Specifically, the multi-head attention mechanism is a computational collection of multiple independent layers of attention, which can extract feature information from multiple dimensions. The expectation pooling combines probabilistic thinking with global pooling. This approach helps to reduce model parameters and enhance the model performance. The combination of CNN and GRU enables further extraction of high-level features in sequences. In addition, the study shows that appropriate hyperparameters have a positive impact on the model performance. Physicochemical properties can be used to supplement characterization information to improving model performance. The experimental results show that MAHyNet has better performance than other models.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs. In order to capture the features of the network structure and fully explore direct or indirect relationships between nodes, we propose the method of multiple similarity, which combines similarity fusion matrices with Random Walk with Restart (RWR) and cosine similarity. Then, we use GCN to extract multi-layer low-dimensional embedding features. Unlike traditional GCN methods, we incorporate Multiple Kernel Learning (MKL). Finally, we use the Dual Laplace Regularized Least Squares method to predict novel DTIs through combinatorial kernels in drug and target spaces. We conduct experiments on a golden standard dataset, and demonstrate the effectiveness of our proposed model in predicting DTIs through showing significant improvements in Area Under the Curve (AUC) and Area Under the Precision-Recall Curve (AUPR). In addition, our model can also discover some new DTIs, which can be verified by the KEGG BRITE Database and relevant literature.

Cooperative co-evolutionary algorithms (CCEAs) have witnessed giant success in solving large-scale optimization problems (LSOPs). However, most existing CCEAs use low-dimensional EAs to optimize the decomposed sub-problems. Such utilization of low-dimensional EAs may limit the effectiveness of CCEAs because some of the decomposed sub-problems may still be high-dimensional. Since there exist many non-decomposition based large-scale EAs, it is interesting to investigate the optimization effectiveness of CCEAs by using these non-decomposition based large-scale EAs to solve the decomposed sub-problems. To this end, this paper incorporates two state-of-the-art large-scale swarm optimizers into CCEAs with five state-of-the-art decomposition strategies to solve LSOPs. Experiments conducted on the CEC'2010 and CEC'2013 LSOP benchmark sets have shown that the two large-scale swarm optimizers help CCEAs with the five decomposition strategies achieve much better performance than the most widely used low-dimensional EA.

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.

The intermolecular interactions between proteins and ligands occur through site-specific amino acid residues in the proteins, and the identification of these key residues plays a critical role in both interpreting protein function and facilitating drug design based on virtual screening. In general, the information about the ligands-binding residues on proteins is unknown, and the detection of the binding residues by the biological wet experiments is time consuming. Therefore, many computational methods have been developed to identify the protein-ligand binding residues in recent years. We propose GraphPLBR, a framework based on Graph Convolutional Neural (GCN) networks, to predict protein-ligand binding residues (PLBR). The proteins are represented as a graph with residues as nodes through 3D protein structure data, such that the PLBR prediction task is transformed into a graph node classification task. A deep graph convolutional network is applied to extract information from higher-order neighbors, and initial residue connection with identity mapping is applied to cope with the over-smoothing problem caused by increasing the number of graph convolutional layers. To the best of our knowledge, this is a more unique and innovative perspective that utilizes the idea of graph node classification for protein-ligand binding residues prediction. By comparing with some state-of-the-art methods, our method performs better on several metrics.

Artificial intelligence (AI) has fast developed nowadays especially in the deep learning field that most of the deep neural networks pursue the better problem-solving ability by making the network models deeper, larger, and more complex. However, too large and too complex AI algorithms/models require too large computational burden, which is not reality in academic researches nor the right way to real human intelligence. Moreover, in another research branch of AI named evolutionary computation (EC), which is inspired by the biological evolution of nature and swarm intelligence behaviours, will the EC algorithms become more efficient with larger and more complex algorithm model when solving complicated optimization problems like the large-scale optimization problems (LSOPs)? To this concern, this paper investigates whether some existing large-scale optimization EC algorithms can further improve their performance in solving LSOPs by only increasing the population size. We select 12 representative algorithms for investigation, including 4 standard EC algorithms and 8 well-known large-scale optimization algorithms. Then, we adopt the widely-used IEEE Congress on Evolutionary Computation (CEC 2010) LSOPs benchmark test suite to compare the performance of the same algorithms with different population sizes. The experimental results show that simply increasing the population size does not necessarily improve the performance of algorithms in solving LSOPs.

Electric Vehicle Routing Problem (EVRP) is considerably challenging due to the capacity and electricity constraints of electric vehicles (EVs). Most existing studies on EVRP consider no limits on charging times when optimizing the routes of EVs. However, due to the long time of charging, the charging times of EVs are usually limited due to the urgent service demands of customers. To simulate this practical problem, this paper first formulates the EVRP with both capacity and charging time constraints (EVRP-CC). To tackle this new optimization problem, this paper further devises a two-stage solution construction method for ant colony optimization (ACO) to build feasible solutions to EVRP-CC. Subsequently, we embed the proposed method into five popular and classical ACO algorithms, namely ant system (AS), ranking based ant system (Rank-AS), elite ant system (EAS), max-min ant system (MMAS), and ant colony system (ACS), to solve EVRP-CC. Extensive experiments conducted on several instances generated from the widely used EVRP benchmark set demonstrate that the proposed solution construction method is effective to help ACO to solve EVRP-CC. In particular, Rank-AS with the proposed solution construction method achieves the best overall performance in solving EVRP-CC.

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question “Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?” If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called nameBIH. Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.

Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.

In the drug discovery process and repurposing of existing drugs, accurately identifying ligands with high binding affinity to proteins is a very critical step. However, it sinks a lot of time and resources to detect the protein-ligand binding affinity through biological experiments. Therefore, it is very necessary to develop an accurate and reliable computational method to predict the binding affinity between protein and ligand. At present, some computational methods have been proposed to predict the protein-ligand binding affinity, but the absence of protein-ligand complexes structures restricts some predictive methods that require input the complexes structures. In this paper, a novel deep-learning-based method is proposed, named DPLA, to predict binding affinity by integrating multilevel information of protein and ligand. More specifically, our model extracted some important information, such as sequence representation, structural property representation of amino acids in protein and protein binding pocket, MACCS key ligand molecular fingerprint and ligand molecular network features. This method was tested on the PDBbind core set, and we compared it with some recent state-of-art protein-ligand affinity prediction methods. The excellent performance shows that DPLA is an accurate and reliable method for affinity prediction.

Genetic algorithm (GA) is a widely used probabilistic search optimization algorithm. In the GA, selection is an important operator to guarantee the quality of solution. Therefore, the behavior of selection operator makes a great effect on the performance of the algorithm. This paper designs a new and efficient selection operator for GA base on the idea of promotion competition. This operator simulates the rule and process of promotion competition to protect the well perform chromosomes and eliminates poor chromosomes. This is a fundamental but significant research issue in GA that may be adopted into any existing GA variants to replace any other selection operators. We design four types of experiments to comprehensively verify the behavior of the proposed promotion selection operator, by comparing it with five other existing and commonly used selection operators. The results show that promotion selection operator has a general good performance in enhancing GA in terms of solution quality, convergence speed, and running time.

Multimodal optimization problems (MMOPs) require the algorithm to locate multiple global optima and also achieve a certain accuracy on the found optima. When applying particle swarm optimization (PSO) to solve MMOPs, a fixed population communication topology may not be sufficient to handle these two requirements simultaneously. In this paper, a novel PSO with hybrid ring topology, termed HRTPSO, is proposed for MMOPs. In the early evolutionary process of HRTPSO, a sparse topology is constructed to enhance the population diversity to help locate multiple optima, while in the later evolutionary process of HRTPSO, the population communication topology is switched to a relatively dense topology for improving the convergence efficiency on the found optima. The switch of topology is controlled by a threshold and its effect is also analyzed in this paper. Experimental results on the 20 multimodal functions in CEC'2013 benchmark set show that HRTPSO has better performance than the other six multimodal optimization algorithms.