arXiv subjects

Sergio Nesmachnow

Publications and source records attributed to Sergio Nesmachnow.

10 recordsLinked to original sources

arXiv2026

Robust Multi-Objective Optimization for Bicycle Rebalancing in Shared Mobility Systems

Dock-based bike-sharing systems exhibit spatial imbalances between bicycle supply and user demand, often addressed through overnight truck-based rebalancing. This work studies static overnight rebalancing under demand uncertainty modeled as a tri-objective optimization problem. The objectives minimize total travel distance, expected unmet demand, and a robustness-oriented unmet demand measure over high-demand scenarios. Route plans are evaluated via a recourse simulation that enforces truck loads and station capacity constraints across multiple demand realizations. The robustness objective supports selecting plans that reduce peak-demand service degradation. Trade-off solutions are approximated with Non-dominated Sorting Genetic Algorithm II using a permutation--partition encoding and domain-specific relocation operators, including a biased best-improvement move for station relocation. Experiments on the real Barcelona Bicing system with 460 stations show well-distributed Pareto sets and substantial contributions to the reference non-dominated set. Greedy constructive baselines mainly yield extreme solutions and are often dominated.

cs.NE↗

arXiv2025

Adversarial attacks to image classification systems using evolutionary algorithms

Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This article explores an approach to generate adversarial attacks against image classifiers using a combination of evolutionary algorithms and generative adversarial networks. The proposed approach explores the latent space of a generative adversarial network with an evolutionary algorithm to find vectors representing adversarial attacks. The approach was evaluated in two case studies corresponding to the classification of handwritten digits and object images. The results showed success rates of up to 35% for handwritten digits, and up to 75% for object images, improving over other search methods and reported results in related works. The applied method proved to be effective in handling data diversity on the target datasets, even in problem instances that presented additional challenges due to the complexity and richness of information.

cs.NE↗

arXiv2025

Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm

This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR configurations by using a parallel evolutionary algorithm. The experimental analysis demonstrates that significant improvements over the standard configuration can be attained in terms of power consumption, with no noteworthy loss in the QoS.

cs.NE↗

arXiv2024

Solving the waste bin location problem with uncertain waste generation rate: a bi-objective robust optimization approach

An efficient Municipal solid waste (MSW) system is critical to modern cities in order to enhance sustainability and livability of urban life. With this aim, the planning phase of the MSW system should be carefully addressed by decision makers. However, planning success is dependent on many sources of uncertainty that can affect key parameters of the system, e.g., the waste generation rate in an urban area. With this in mind, this paper contributes with a robust optimization model to design the network of collection points (i.e., location and storage capacity), which are the first points of contact with the MSW system. A central feature of the model is a bi-objective function that aims at simultaneously minimizing the network costs of collection points and the required collection frequency to gather the accumulated waste (as a proxy of the collection cost). The value of the model is demonstrated by comparing its solutions with those obtained from its deterministic counterpart over a set of realistic instances considering different scenarios defined by different waste generation rates. The results show that the robust model finds competitive solutions in almost all cases investigated. An additional benefit of the model is that it allows the user to explore trade-offs between the two objectives.

math.OC↗

arXiv2022

Evolutionary latent space search for driving human portrait generation

This article presents an evolutionary approach for synthetic human portraits generation based on the latent space exploration of a generative adversarial network. The idea is to produce different human face images very similar to a given target portrait. The approach applies StyleGAN2 for portrait generation and FaceNet for face similarity evaluation. The evolutionary search is based on exploring the real-coded latent space of StyleGAN2. The main results over both synthetic and real images indicate that the proposed approach generates accurate and diverse solutions, which represent realistic human portraits. The proposed research can contribute to improving the security of face recognition systems.

cs.CV↗

arXiv2022

Waste bins location problem: A review of recent advances in the storage stage of the Municipal Solid Waste reverse logistic chain

Municipal Solid Waste systems have important economic, social, and environmental impacts for society. Within the diverse stages of the Municipal Solid Waste reverse logistic chain, the waste bin location problem consists in properly locating bins in the corresponding urban area to store waste produced by the citizens. This stage has a large impact in the overall efficiency of the whole system. Thus, several researchers have addressed the location problem considering different optimization criteria and approaches. This article presents a comprehensive review of recent advances on the Waste Bins Location Problem, with the main goal of serving as a reference point for decision-makers in this area. The main findings indicate that several optimization criteria and resolution approaches have been applied, but few proposals have simultaneously optimized bins location and waste collection, or considered uncertainty of the model parameters and integrated approaches.

physics.soc-ph↗

arXiv2021

Using Cosmic Rays detected by HST as Geophysical Markers I: Detection and Characterization of Cosmic Rays

The Hubble Space Telescope (HST) has been operational for over 30 years and throughout that time it has been bombarded by high energy charged particles colloquially referred to as cosmic rays. In this paper, we present a comprehensive study of more than 1.2 billion cosmic rays observed with HST using a custom written python package, \texttt{HSTcosmicrays}, that is available to the astronomical community. We analyzed $75,908$ dark calibration files taken as part of routine calibration programs for five different CCD imagers with operational coverage of Solar Cycle 23 and 24. We observe the expected modulation of galactic cosmic rays by solar activity. We model the observed energy-loss distributions to derive an estimate of 534 $\pm$ 117 MeV for the kinetic energy of the typical cosmic ray impacting HST. For the three imagers with the largest non-uniformity in thickness, we independently confirm the overall structure produced by fringing analyses by analyzing cosmic ray strikes across the detector field of view. We analyze STIS/CCD observations taken as HST crosses over the South Atlantic Anomaly and find a peak cosmic-ray particle flux of $\sim1100$ $particle/s/cm^2$. We find strong evidence for two spatially confined regions over North America and Australia that exhibit increased cosmic-ray particle fluxes at the $5σ$ level.

astro-ph.IM↗

arXiv2021

Exact and heuristic approaches for multi-objective garbage accumulation points location in real scenarios

Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bahía Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.

cs.OH↗

arXiv2020

Parallel/distributed implementation of cellular training for generative adversarial neural networks

Generative adversarial networks (GANs) are widely used to learn generative models. GANs consist of two networks, a generator and a discriminator, that apply adversarial learning to optimize their parameters. This article presents a parallel/distributed implementation of a cellular competitive coevolutionary method to train two populations of GANs. A distributed memory parallel implementation is proposed for execution in high performance/supercomputing centers. Efficient results are reported on addressing the generation of handwritten digits (MNIST dataset samples). Moreover, the proposed implementation is able to reduce the training times and scale properly when considering different grid sizes for training.

cs.DC↗

arXiv2019

Soft computing methods for multiobjective location of garbage accumulation points in smart cities

This article describes the application of soft computing methods for solving the problem of locating garbage accumulation points in urban scenarios. This is a relevant problem in modern smart cities, in order to reduce negative environmental and social impacts in the waste management process, and also to optimize the available budget from the city administration to install waste bins. A specific problem model is presented, which accounts for reducing the investment costs, enhance the number of citizens served by the installed bins, and the accessibility to the system. A family of single- and multi-objective heuristics based on the PageRank method and two mutiobjective evolutionary algorithms are proposed. Experimental evaluation performed on real scenarios on the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates the effectiveness of the proposed approaches. The methods allow computing plannings with different trade-off between the problem objectives. The computed results improve over the current planning in Montevideo and provide a reasonable budget cost and quality of service for Bahia Blanca.

cs.AI↗