Metaheuristics have been employed in solving several optimization and NP-hard problems owing to their ability to avoid local optima entrapment, flexibility and robustness, simplicity, and reasonable ...computational time. In this work, an empirical analysis is carried out on the application of metaheuristics in solving the protein structure prediction (PSP) problem. The PSP problem entails predicting a three-dimensional (3D) structure of proteins based on the amino acid sequences. This study employs the Ab Initio method, which relies on the thermodynamic properties of the proteins, such as the minimal free energy levels, bond angles, and torsional angles, in determining the protein structure. Five real protein sequences, namely 1CB3, 1BXL, 2H3S, 1TZ4, and 1CRN from the Protein Data Bank (PDB), and 11 metaheuristics including DE, GA, PSO, DSO, ACO, and HS were employed in the empirical analysis. Moreover, extensive Monte Carlo simulations were carried out to generate the results of the empirical analysis and these results were statistically tested using the Friedman test.
Signed graphs are well-suited for modeling social networks as they capture both positive and negative relationships. Signed graph neural networks (SGNNs) are commonly employed to predict link signs ...(i.e., positive and negative) in such graphs due to their ability to handle the unique structure of signed graphs. However, real-world signed graphs are vulnerable to malicious attacks by manipulating edge relationships, and existing adversarial graph attack methods do not consider the specific structure of signed graphs. SGNNs often incorporate balance theory to effectively model the positive and negative links. Surprisingly, we find that the balance theory that they rely on can ironically be exploited as a black-box attack. In this paper, we propose a novel black-box attack called balance-attack that aims to decrease the balance degree of the signed graphs. We present an efficient heuristic algorithm to solve this NP-hard optimization problem. We conduct extensive experiments on five popular SGNN models and four real-world datasets to demonstrate the effectiveness and wide applicability of our proposed attack method. By addressing these challenges, our research contributes to a better understanding of the limitations and resilience of robust models when facing attacks on SGNNs. This work contributes to enhancing the security and reliability of signed graph analysis in social network modeling. Our PyTorch implementation of the attack is publicly available on GitHub: https://github.com/JialongZhou666/Balance-Attack.git.
In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources ...to meet diverse service requirements. We propose an efficient decomposition algorithm for solving this NP-hard problem. The proposed algorithm decomposes the large-scale hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined; and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the global solution of the NS problem. We demonstrate the effectiveness and efficiency of the proposed algorithm via numerical experiments.
The optimal deadlock avoiding, deadlock recovery, as well as deadlock detection in Petri nets are the NP-hard problems. For this reason, heuristic algorithms for finding the approximate solutions of ...such problems are of a great interest. In the paper a heuristic method of deadlock recovery for the S 3 PR nets, which are often used for modelling of manufacturing systems, is proposed. The method is based on the idea of resetting the process instances in the S 3 PR nets and a heuristic for breaking the cycles in the wait-for graphs. The method does not require any time consuming operations such as construction of the reachability graph, explicit detecting of all reachable deadlocks or all elementary cycles in a wait-for graph, hence it is quick. The simple recovery transitions are added to the net providing recovery from both global and local deadlocks.
The amalgamation of Cloud Computing and Internet-of-Things (IoT), i.e., Cloud-of-Things (CoT), has emerged as one of the indispensable technologies in the IT and business world. The success of CoT ...depends on the efficient services provided by cloud computing to the requests generated by IoT devices. The efficient management of IoT services on cloud virtualized resources, an NP-hard problem, is the major component in developing trust and reliability among IoT users. This research proposes a novel chaotic and transfer functionassisted modified grey wolf optimizer (MGWO) algorithm to schedule IoT requests on virtual machines in cloud computing. The proposed MGWO algorithm utilizes a transfer function to convert the wolf into the solution and a chaotic circle map to improve the initial population's diversity, which leads to faster convergence and better exploitation and exploration. The MGWO algorithm's performance is superior to the generic version of the GWO algorithm and its variants.
As a systematic diagnosis model, the dependency matrix describes the relationship between failure mode and test, and is widely utilized in the industry. In engineering, the dependency matrix is ...transformed or reshaped for different usages, and the related dependency matrix-based methods are various and complicated. However, the related approaches are not summarized systematically. Here, we provide a concise and comprehensive review of the related approaches, which are concluded into three categories: dependency matrix generation, test selection, and fault diagnosis. For each part, the conventional and the latest methods are introduced. This review is dedicated to introducing the current status of the dependency matrix-based method in fault diagnosis. We hope the review can provide an effective direction to overcome the challenges that the current methods face.
One of the vital activity in the area of data analysis is data clustering. It is an unsupervised learning used to make clusters of similar objects. The similarity between the objects are calculated ...by distance between the two clusters. Clustering algorithms are widely applied in solving real-time problems. The inter-cluster distance should be high and intra-cluster distance must be low for optimal clustering. The finding of optimal cluster is always a NP-hard problem. In this research, cat swarm optimization (CSO) is used to solve partitional clustering problem. CSO is a very efficient algorithm to solve the optimization problem but many times it stuck in premature convergence. Hence, an improved CSO is proposed to deal with these problems. The proposed algorithm is tested over some standard datasets and a comparative analysis is being done with some standard clustering algorithms. The results obtained reflects the supremacy of the proposed algorithm over existing clustering algorithms.
We present a generalization of the binary paint shop problem (BPSP) to tackle an automotive industry application, the multi-car paint shop (MCPS) problem. The objective of the optimization is to ...minimize the number of color switches between cars in a paint shop queue during manufacturing, a known NP-hard problem. We distinguish between different sub-classes of paint shop problems, and show how to formulate the basic MCPS problem as an Ising model. The problem instances used in this study are generated using real-world data from a factory in Wolfsburg, Germany. We compare the performance of the D-Wave 2000Q and Advantage quantum processors to other classical solvers and a hybrid quantum-classical algorithm offered by D-Wave Systems. We observe that the quantum processors are well-suited for smaller problems, and the hybrid algorithm for intermediate sizes. However, we find that the performance of these algorithms quickly approaches that of a simple greedy algorithm in the large size limit.
In this research paper, we employ D-Wave's quantum annealer to address the NP-hard problem of optimizing new metro station locations while considering both points of interest and existing stations. ...By formulating the problem as Quadratic Unconstrained Binary Optimization (QUBO) and Binary Quadratic Model (BQM), our approach harnesses the power of quantum computing to yield superior solutions compared to classical methods. This research has the potential to revolutionize urban transportation planning by efficiently balancing accessibility to points of interest and minimizing congestion around existing stations, offering a compelling solution to a complex urban development challenge. Notably, we emphasize the NP-hard nature of this problem, underlining its computational complexity and the significance of our quantum computing approach.
A Novel Genetic Algorithm to Bayesian Networks Structure Learning Parrela, Frederico A. O.; Bessani, Michel; Guimaraes, Frederico G. ...
2023 3rd International Conference on Robotics, Automation and Artificial Intelligence (RAAI),
2023-Dec.-14
Conference Proceeding
Bayesian Networks (BN) provide a robust way to represent joint probability distributions (JPD) using a direct acyclic graph (DAG) that encodes the independence between variables (nodes). Learning ...Bayesian networks from data is considered an NP-Hard problem, and there is no consensus on the best methods. A novel genetic algorithm (GA) has been proposed for learning BN from data. The GA is based on a proposed representation that does not allow graphs with self-loop or loops with their neighbor nodes to be represented. Furthermore, it is better at exploiting the search space since similar DAGs possess more similar representations than the conventional adjacency matrix representation. The Proposed GA was compared to six other well-established algorithms for BN structure learning using five different databases. The results suggest that the proposed GA outperforms all the other algorithms in finding A DAG more similar to the ground truth for four of the five used databases.
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