752 publications from this institution
Detecting groups within a set of interconnected nodes is a widely addressed problem that can model a diversity of applications. Unfortunately, detecting the optimal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to providing an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti–Fortunato–Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform competitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come.
No abstract is provided for this article.
A supply chain is a system which moves products from a supplier to customers. The supply chains are ubiquitous. They play a key role in all economic activities. Inspired by biological principles of nutrients' distribution in protoplasmic networks of slime mould Physarum polycephalum we propose a novel algorithm for a supply chain design. The algorithm handles the supply networks where capacity investments and product flows are variables. The networks are constrained by a need to satisfy product demands. Two features of the slime mould are adopted in our algorithm. The first is the continuity of a flux during the iterative process, which is used in real-time update of the costs associated with the supply links. The second feature is adaptivity. The supply chain can converge to an equilibrium state when costs are changed. Practicality and flexibility of our algorithm is illustrated on numerical examples.
No abstract is provided for this article.
Welcome to NPSec 2009, the fifth Workshop on Secure Network Protocols, co-located with ICNP. This year, the workshop is being held in Princeton, NJ - a wonderful opportunity to observe rich fall colors at the East Coast while listening to stimulating papers at the workshop!
The variations of gravity were measured with a high precision LaCoste-Romberg D gravimeter during a total solar eclipse to investigate the effect of a solar eclipse on the gravitational field. The observed anomaly $(7.0\ifmmode\pm\else\textpm\fi{}2.7)$$\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}8}$ ${\mathrm{m}/\mathrm{s}}^{2}$during the eclipse implies that there may be a shielding property of gravitation.
With the extensive application of numerical simulations in engineering fields such as mining, tunnelling, and petroleum, enhancing the accuracy of simulation tools to ensure their reliability has become a widely discussed issue. Using basic rock mechanics experiments to validate numerical models often encounters problems when these models are applied to more complex tests, resulting in distorted simulation results. To address this issue, this study uses single impact tests indicative of percussive drilling as an example, focusing on two crucial aspects during the bit-rock interaction: bit energy loss and the breakage morphology, including metrics for the extent of rock cracking. Seven criteria related to these two aspects were selected to validate the accuracy of the FDEM model. The simulation results were validated against two types of hard sedimentary rocks: St Anne limestone and Rhune sandstone. The numerical simulation results at different impact energies agreed well with experimental results across these seven criteria. The FDEM simulation results accurately reproduced the experimental observations, capturing the different crack morphologies and their evolution as observed in experimental CT scans and high-speed video recordings. The presence of median cracks in limestone and absence in sandstone, as observed in experiments, was reproduced by the simulations. Notably, the simulation results reveal that the generation of side cracks is not necessarily caused by the closure of median cracks because median cracks do not always initiate, as postulated in the literature. Instead, FDEM simulation results indicate that the propagation of side cracks is primarily caused by the tearing induced by the elastic recovery of the rock.
No abstract is provided for this article.
This editorial paper reviews the Special Issue “New Advances in High-Dimensional and Non-asymptotic Statistics” and summarizes the ten collected papers [...]
The bat algorithm is a bio-inspired algorithm that uses frequency tuning and echolocation characteristics. This chapter introduces the fundamental idea of the bat algorithm and its variants. A demo implementation is also given, followed by the discussions of convergence and stability analysis.
No abstract is provided for this article.
This paper aims to solve the fault tolerant control problem of a wind turbine benchmark. A hierarchical controller with model predictive pre-compensators, a global model predictive controller and a supervisory controller is proposed. In the model predictive pre-compensator, an extended Kalman Filter is designed to estimate the system states and various fault parameters. Based on the estimation, a group of model predictive controllers are designed to compensate the fault effects for each component of the wind turbine. The global MPC is used to schedule the operation of the components and exploit potential system-level redundancies. Extensive simulations of various fault conditions show that the proposed controller has small transients when faults occur and uses smoother and smaller generator torque and pitch angle inputs than the default controller. This paper shows that MPC can be a good candidate for fault tolerant controllers, especially the one with an adaptive internal model combined with a parameter estimation and update mechanism, such as an extended Kalman Filter.
In the context of globalization and rapid technological advancement, the introduction of Artificial Intelligence (AI) has brought new opportunities and challenges to Human Resource Management (HRM). This study constructs an evolutionary game model to explore the strategy choices and evolutionary paths of enterprises and employees in HRM value co-creation with AI involvement. We numerically simulated the dynamic evolution of strategies under different scenarios, revealing the equilibrium characteristics of strategic interactions between enterprises and employees in the AI context. The study finds that, first, the evolutionary game system between enterprises and employees converges to two equilibrium points: {cooperation, active} and {non-cooperation, passive}. Overall, the probability of the former is 2.39 times greater than that of the latter. Second, higher initial probabilities of cooperation and active involvement, along with lower costs for cooperation and active involvement, facilitate the system’s evolution towards the {cooperation, active} equilibrium. Third, enterprises are more sensitive to the benefit distribution ratio than employees. This study provides theoretical support for effectively conducting HRM practices in the AI era through systematic analysis of HRM value co-creation behavior, along with practical policy recommendations.
Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.
Genetic algorithm is one of the first evolutionary algorithms, which paces the way for contemporary evolutionary computation. This chapter introduces the basic principle of the genetic algorithm and its fundamental genetic operators: crossover, mutation, and selection.
No abstract is provided for this article.
The bat algorithm (BA) is a population-based algorithm and it typically has good convergence for solving many optimization problems. This chapter first introduces the original bat algorithm with detailed descriptions of the main steps, followed by demo codes in both Matlab and C++. Discussions of main parameters and their settings are also carried out.
