Linear models

Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges

In the last two decades, a variety of different types of multiobjective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. The experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

The Critical Node Detection Problem in networks: A survey.

Finding an optimal set of nodes, called key players, whose activation (or removal) would maximally enhance (or degrade) certain network functionality, is a fundamental class of problems in network science1,2. Potential applications include network immunization3, epidemic control4, drug design5, and viral marketing6. Due to their general NP-hard nature, those problems typically cannot be solved by exact algorithms with polynomial time complexity7. Many approximate and heuristic strategies have been proposed to deal with specific application scenarios1,2,8-12. Yet, we still lack a unified framework to efficiently solve this class of problems. Here we introduce a deep reinforcement learning framework FINDER, which can be trained purely on small synthetic networks generated by toy models and then applied to a wide spectrum of influencer finding problems. Extensive experiments under various problem settings demonstrate that FINDER significantly outperforms existing methods in terms of solution quality. Moreover, it is several orders of magnitude faster than existing methods for large networks. The presented framework opens up a new direction of using deep learning techniques to understand the organizing principle of complex networks, which enables us to design more robust networks against both attacks and failures.

/pdf/finding-key-players-in-complex-networks-through-deep-1ghpg9i9v5.pdf

Finding key players in complex networks through deep reinforcement learning.

Component-cardinality-constrained critical node problem in graphs

In this paper, we propose a polynomial-time algorithm for solving the Component-Cardinality-Constrained Critical Node Problem (3C-CNP) on bipartite permutation graphs. This problem, which is a variant of the well-known Critical Node Detection problem, consists in finding the minimal subset of nodes within a graph, the deletion of which results in a set of connected components of at most K nodes each one, where K is a given integer. The proposed algorithm is a dynamic programming scheme of time complexity $$O(nK^2)$$
 , where n is the number of nodes. To provide evidences of algorithm’s efficiency, different experiments have been performed on randomly generated graphs.

A polynomial-time algorithm for finding critical nodes in bipartite permutation graphs

Cyber systems have become ubiquitous and indispensable in our daily life, and the extent of our dependence on them has increasingly grown in all fields including: education, business, industry and government. Those systems make intensive use of data and information and are therefore exposed to more potential cyber attacks. Thereby, the need for reliable approaches to protect them has increased. One of the key elements for guaranteeing the security of cyber systems is to identify the origin (the source) of the attack. In this paper, we describe a new approach to estimate both the source and the start time of a virus outbreak in complex networks (which include cyber systems) using partial information about the diffusion process, obtained through observing only a subset of nodes. Our approach uses a linear regression method on the partial obtained data, based on the fact that there is a linear correlation observed between the relative infection time of a node and its effective distance from the source. The experimental results showed that our approach is able to give an estimation of the source and the start time in, respectively, few hops from the actual source, and few time-units from the real start time.

Identifying the Cyber Attack Origin with Partial Observation: A Linear Regression Based Approach

Solving CSP is in general NP-Complete. However, there are various subsets of CSPs that can be solved in polynomial time. Some of them can be identified by analyzing their structure. Unfortunately the proposed methods for exploiting these structural proprieties are not efficient in practice. So exploiting these structural properties for solving CSPs is a crucial challenge. In this paper, we propose ecient algorithms which exploit these structural proprieties, for both sequential and parallel resolutions. Some experiments done on academic benchmarks show the eciciency of our approach.

/pdf/solving-hypertree-structured-csp-sequential-and-parallel-401q8zob2w.pdf

Mohammed Lalou

Papers

The Critical Node Detection Problem in networks: A survey.

Component-cardinality-constrained critical node problem in graphs

A polynomial-time algorithm for finding critical nodes in bipartite permutation graphs

Identifying the Cyber Attack Origin with Partial Observation: A Linear Regression Based Approach

Solving hypertree structured CSP : Sequential and parallel approaches