Variety Of Algorithms Research Articles

Abstraction and Path Computation for Video Game Path Finding with Changing Maps

Efficient grid-based path finding is important in video games especially for larger maps and when moving many agents. Algorithms based on abstraction have an order of magnitude faster search time performance than A* at the cost of a small amount of memory and increased suboptimality. Some approaches also compute and store paths to improve search performance. This paper evaluates new and existing algorithm variants for abstraction and path computation and investigates their performance for video game path finding with map changes. The results show that abstraction has significant advantages over A* and can be implemented efficiently for changing maps. Computing, storing, and reusing paths also has benefits especially when several searches can be performed before the map changes.

Conformal prediction with local weights: randomization enables robust guarantees

Abstract In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly popular framework for building such intervals with distribution-free guarantees, but these guarantees only ensure marginal coverage: the probability of coverage is averaged over both the training and test data, meaning that there might be substantial undercoverage within certain subpopulations. Instead, ideally we would want to have local coverage guarantees that hold for each possible value of the test point’s features. While the impossibility of achieving pointwise local coverage is well established in the literature, many variants of conformal prediction algorithm show favourable local coverage properties empirically. Relaxing the definition of local coverage can allow for a theoretical understanding of this empirical phenomenon. We propose randomly localized conformal prediction (RLCP), a method that builds on localized CP and weighted CP techniques to return prediction intervals that are not only marginally valid but also offer relaxed local coverage guarantees and validity under covariate shift. Through a series of simulations and real data experiments, we validate these coverage guarantees of RLCP while comparing it with the other local conformal prediction methods.

A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in the transition towards more sustainable and environmentally friendly mobility. The need to design and implement an efficient network that meets the needs of all users motivates us to propose the use of a model based on GNG-type neural networks for the design of the network in a specific geographical area. In this paper, a variant of this iterative neural network algorithm is used with the objective that, from an initial dataset of points in the plane, it calculates a new simplified dataset with the main characteristic that the final set of points maintains the geometric shape and topology of the original set. To demonstrate the capabilities of the algorithm, it is exemplified in a real case, in which the design of an electric vehicle charging network is proposed. This network is built by applying the algorithm, taking as the original set of points the ones formed by the nodes of the gas station network in the geographical area studied. Several tests of running the algorithm for different sizes of the final dataset are performed, showing the differences between the original network and the computationally generated one.

Is Pessimism Provably Efficient for Offline Reinforcement Learning?

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a data set collected a priori. Because of the lack of further interactions with the environment, offline RL suffers from the insufficient coverage of the data set, which eludes most existing theoretical analysis. In this paper, we propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function. Such a penalty function simply flips the sign of the bonus function for promoting exploration in online RL, which makes it easily implementable and compatible with general function approximators. Without assuming the sufficient coverage of the data set (e.g., finite concentratability coefficients or uniformly lower-bounded densities of visitation measures), we establish a data-dependent upper bound on the suboptimality of PEVI for general Markov decision processes (MDPs). When specialized to linear MDPs, it matches the information-theoretic lower bound up to multiplicative factors of the dimension and horizon. In other words, pessimism is not only provably efficient but also, minimax optimal. In particular, given the data set, the learned policy serves as the “best effort” among all policies as no other policies can do better. Our theoretical analysis identifies the critical role of pessimism in eliminating a notion of spurious correlation, which arises from the “irrelevant” trajectories that are less covered by the data set and not informative for the optimal policy. Funding: Z. Yang acknowledges the Simons Institute (Theory of Reinforcement Learning). Z. Wang acknowledges the National Science Foundation [Awards 2048075, 2008827, 2015568, and 1934931], the Simons Institute (Theory of Reinforcement Learning), Amazon, J. P. Morgan, and Two bSigma.

Initial tensor construction and dependence of the tensor renormalization group on initial tensors

We propose a method to construct a tensor network representation of partition functions without singular value decompositions nor series expansions. The approach is demonstrated for one- and two-dimensional Ising models and we study the dependence of the tensor renormalization group (TRG) on the form of the initial tensors and their symmetries. We further introduce variants of several tensor renormalization algorithms. Our benchmarks reveal a significant dependence of various TRG algorithms on the choice of initial tensors and their symmetries. However, we show that the boundary TRG technique can eliminate the initial tensor dependence for all TRG methods. The numerical results of TRG calculations can thus be made significantly more robust with only a few changes in the code. Furthermore, we study a three-dimensional Z2 gauge theory without gauge fixing and confirm the applicability of the initial tensor construction. Our method can straightforwardly be applied to systems with longer range and multisite interactions, such as the next-nearest neighbor Ising model. Published by the American Physical Society 2024

Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm

Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm

Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting

Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution, availability, and international policy. Also, various factors need to be considered in order to make an accurate forecast. By analyzing the current literature, a gap for improvements within this domain exists. Therefore, this work suggests and explores the potential of multi-headed long short-term memory models for gasoline price forecasting, since this issue was not tackled with multi-headed models before. Additionally, since the computational requirements for such models are relatively high, work focuses on lightweight approaches that consist of a relatively low number of neurons per layer, trained in a small number of epochs. However, as algorithm performance can be heavily dependent on appropriate hyper-parameter selections, a modified variant of the particle swarm optimization algorithm is also set forth to help in optimizing the model’s architecture and training parameters. A comparative analysis is conducted using energy data collected from multiple public sources between several contemporary optimizers. The outcomes are put through a meticulous statistical validation to ascertain the significance of the findings. The best-constructed models attained a mean square error of just 0.044025 with an R-squared of 0.911797, suggesting potential for real-world use.

FairDRO: Group fairness regularization via classwise robust optimization

Existing group fairness-aware training methods fall into two categories: re-weighting underrepresented groups according to certain rules, or using regularization terms such as smoothed approximations of fairness metrics or surrogate statistical quantities. While each category has its own strength in applicability or performance when compared to each other, their successful performances are typically limited to specific cases. To that end, we propose a new approach called FairDRO, which takes advantage of both categories through a classwise group distributionally robust optimization (DRO) framework. Our method unifies re-weighting and regularization by incorporating a well-justified group fairness metric into the objective as regularization, but solving it through a principled re-weighting strategy. To optimize our resulting objective efficiently, we adopt an iterative algorithm and consequently develop two variants of FairDRO algorithm depending on the choice of surrogate loss. For in-depth understanding, we derive three theoretical results: (i) a closed-form solution for the correct re-weights; (ii) justifications for using the surrogate losses; and (iii) a convergence analysis of our method. Experimental results show that our algorithms consistently achieve state-of-the-art performance in accuracy-fairness trade-offs across multiple benchmarks, demonstrating scalability and broad applicability compared to existing methods.

Applications and Advances of UCB Algorithms in Dynamic and Contaminated Environments

The Upper Confidence Bound (UCB) algorithm is a widely used approach in the Multi-Armed Bandit (MAB) problem, where the goal is to maximize cumulative rewards over time by selecting the best possible action among several options. The UCB algorithm uses confidence bounds to balance exploration and exploitation, guiding its decision-making. In recent years, researchers have identified significant challenges in applying the UCB algorithm to dynamic and contaminated environments. In such scenarios, the underlying conditions may change over time, making it difficult for standard UCB to adapt, or the data may be polluted by noise and outliers, leading to incorrect estimations of reward distributions. To address these challenges, several variants of the UCB algorithm have been developed. These new approaches are designed to better handle the complexities of changing environments and data contamination, ensuring more robust and reliable performance in these difficult settings. This paper aims to provide a comprehensive review of Robust-UCB (cr-UCB), Sliding Window UCB (SW-UCB) and bandit-over-bandit UCB (BOB-UCB). Focusing on their theoretical foundations, practical applications, and empirical performance. By examining how these algorithms have been adapted to handle the complexities of dynamic and contaminated environments, we found that the adaptability of these algorithms in dynamic environments is significantly improved, and they can effectively reduce decision-making errors caused by data pollution, thus providing a more reliable solution to the multi-armed bandit problem.

Enhanced Data Security Using 5x5 Hill Cipher with Modular 53

This research presents an optimized approach to the Hill Cipher encryption and decryption algorithm using a 5x5 matrix and modular 53 arithmetic. The traditional Hill Cipher, a well-known symmetric key algorithm, typically utilizes smaller matrices and modular arithmetic, which may not provide sufficient security for contemporary applications. By expanding the key matrix to a 5x5 structure and adopting a larger modulus of 53, the complexity and security of the cipher are significantly enhanced. The study details the methodology for constructing and implementing the 5x5 key matrix, as well as the processes for encryption and decryption under the modular 53 system. The computational efficiency and security improvements achieved through this optimization are analyzed. Comparative assessments with the conventional Hill Cipher demonstrate that the enhanced approach offers superior resistance against cryptographic attacks while maintaining manageable computational requirements. The results of this research indicate that the proposed optimized Hill Cipher can serve as a robust encryption method suitable for securing sensitive data in various modern applications. This study contributes to the field of cryptography by providing a more secure and efficient variant of the classical Hill Cipher algorithm

Partition function approach to non-Gaussian likelihoods: macrocanonical partitions and replicating Markov-chains

Monte-Carlo techniques are standard numerical tools for exploring non-Gaussian and multivariate likelihoods. Many variants of the original Metropolis-Hastings algorithm have been proposed to increase the sampling efficiency. Motivated by Ensemble Monte Carlo we allow the number of Markov chains to vary by exchanging particles with a reservoir, controlled by a parameter analogous to a chemical potential μ, which effectively establishes a random process that samples microstates from a macrocanonical instead of a canonical ensemble. In this paper, we develop the theory of macrocanonical sampling for statistical inference on the basis of Bayesian macrocanonical partition functions, thereby bringing to light the relations between information-theoretical quantities and thermodynamic properties. Furthermore, we propose an algorithm for macrocanonical sampling, 𝙰𝚟𝚊𝚕𝚊𝚗𝚌𝚑𝚎 𝚂𝚊𝚖𝚙𝚕𝚒𝚗𝚐, and apply it to various toy problems as well as the likelihood on the cosmological parameters Ωm and w on the basis of data from the supernova distance redshift relation.

Analytical modeling and computational optimization for a 1-DOF compliant mechanism

The first natural frequency directly affects the operational conditions of compliant mechanisms in precision engineering systems. To address this challenge, a computational method based on the surface response method is proposed to optimize the frequency of a new one degree of freedom (DOF) compliant mechanism. Initially, a 1-DOF 3D model of a compliant mechanism is built. The dynamic equation and the frequency response are formulated via equivalent stiffness and the Lagrange method. Subsequently, a series of numerical simulations are conducted to find the fundamental frequency of the mechanism. The initial dimensions of the flexible joints are determined, and the initial frequency is analyzed by using finite element analysis. Next, the flexible joints in the designed mechanism are optimized by a variant of the genetic algorithm. The optimized dimensions of the mechanism are found with the thickness of the circular joint of 1.10 mm, the thickness of the leaf joint of 1.19 mm, and the length of the leaf joint of 54.50 mm. The optimized result showed that there is a significant improvement in the frequency of the mechanism, increasing from an initial design of 53.218 Hz to an optimal design of 75.927 Hz with an improvement of 42.6%. This study provides important reference materials for future research on compliant mechanisms.

Decoding LDPC Codes with a Regular Check Matrix Structure Using the Bit Flip Method

Problem statement: low-density LDPC codes have become very popular in modern communication systems, which make it possible to implement decoders for them in practice. In conditions of severe limitations of equipment performance, the task of searching and researching the characteristics of decoding algorithms for these codes is urgent. The aim of the work is to find a simplified algorithm for decoding LDPC codes and to study the noise immunity of the proposed algorithm. The methods used: to solve this problem computer simulation is used to compare the noise immunity of known decoding algorithms and the proposed algorithm. Scientific novelty: the noise immunity characteristics of the proposed Bit Flip algorithm for decoding LDPC code with a regular structure of the check matrix using gradient descent and the procedure for escaping the local minimum are investigated. Result: a specific variant of the Bit Flip algorithm for LDPC code with a regular structure check matrix using gradient descent and a procedure for escaping the local minimum is proposed. The results of computer simulation are presented. Theoretical significance: the characteristics of noise immunity for the proposed variant of the LDPC code decoding algorithm with a regular structure of the check matrix are investigated. Practical significance: when using the proposed LDPC code decoding algorithm in the receiver, it is possible to calculate the parameters of the communication system based on the available dependencies of the error coefficient on the signal-to-noise ratio.

Способи зниження складності обчислень при розрахунках потокорозподілу в мережах нестисливої рідини при фіксованих коефіцієнтах гідравлічного опору

Based on the analysis of the process of solving the problem of flow distribution calcula-tion in compressible fluid systems, which boils down to finding the root of a nonlinear system of equations using the Newton — Raphson method, methods of reducing the complexity of calculations at the stages of the iterative process are proposed. The studies belong to the class of hydraulic systems for which the total pressure change on the branch elements is a monotonic smooth function of the flow rate G. For the iterations of the Newton-Raphson method, the stages are analyzed: linearization of the nonlinear system of equations, determination of the discrepancy and error estimation, determination of cost growth and obtaining a new iterative cost value. A method and algorithm for determining the discrepancy and estimating the error with linear complexity relative to the number of branches of graph E is proposed. A method of reducing the number of unknowns in the system of flow distribution equations at the stage of forming a system of linear equations based on the use of the nodal convolution method is described. A number of variants of heuristic algorithms for determining the parameter that takes into account the rate of in-crease in costs when forming a new iterative value of costs and their analysis are present-ed.

Historical Elite Differential Evolution Based on Particle Swarm Optimization Algorithm for Texture Optimization with Application in Particle Physics

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called “HE-DEPSO” to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.

Towards the development of measurement and analysis method for aerodynamic noise emission from high speed train: the UIC Aeronoise Project

The increase of railway lines operating at speeds above 250 km/h has shifted the attention towards environmental noise emissions by aerodynamic noise, dominant at these speeds. Adding complexity to the issue, while railway noise emissions at conventional speeds are concentrated in the lower parts of the train, aerodynamic noise emissions are distributed along the train at different heights and depending on the train design. Assessment of aerodynamic noise emissions of high speed train has been endeavored using microphone arrays, implementing different variants of the wave beamforming algorithm. This method involves a quite complex post-processing, and also numerical quantification of results is not immediate. UIC AERONOISE project has aimed at developing a simplified pass-by test methodology capable of quantifying aerodynamic noise emissions from high speed , using exclusively a limited number of standard instrumentation microphones. This methodology has been validated theoretically and is currently undergoing experimental tests. Fundamental points in this methodology are explained in this contribution, together with results achieved so far.

Neural network algorithm with transfer learning and dropout for using a UAV to search the lost target in motion

When performing the rescue mission, how to find a lost target in motion as soon as possible is a very valuable and interesting research topic. To find the optimal flight path of the UAV used to search the lost target in motion under different prior knowledge and interference intensity, this paper proposes a novel optimization technique called neural network algorithm with transfer learning and dropout (TLDNNA), which is a variant of neural network algorithm (NNA) inspired by artificial neural networks. To improve the global search ability and convergence performance of NNA, the multiple transfer mechanism, the generalized mean transfer position, and the random dropout operator driven by the principles of transfer learning and dropout are introduced to TLDNNA. To investigate the performance of TLDNNA, TLDNNA and 12 powerful population-based optimization algorithms are applied to the problem of using a UAV to search the lost target in motion in the designed 9 scenarios. Experimental results show that TLDNNA is remarkably better than the other 12 algorithms in most of the scenarios in terms of solution quality, stability, and convergence performance. In addition, TLDNNA can improve the overall search performance by 18.14% and overall computational efficiency by 3.41 times compared to NNA. The source code of TLDNNA can be obtained by https://github.com/jsuzyy/TLDNNA.

Advancing the deployment and information management of direct air capture: A solution enabled by integrating consortium blockchain system

Direct air capture (DAC) is a critical and emerging Negative Emissions Technology (NET) that directly removes CO2 from the atmosphere, significantly contributing to climate change. However, the deployment and management of large-scale DAC faces challenges such as collections and analysis of energy consumption data, intricate device and system management, emission prediction and operation strategy, precise carbon footprint tracking, etc. This paper proposes the integration of blockchain technology with DAC systems to address these challenges, utilizing blockchain's inherent properties of immutability, security, and transparency. The implementation strategy includes the development of a DAC consortium blockchain system, leveraging a consensus mechanism11Consensus mechanism: The programming and process used in blockchain systems to achieve distributed agreement about the ledger's state or the state of a data set. (Source: Investopedia), ECDSA encryption22Elliptic Curve Digital Signature Algorithm (ECDSA): In cryptography, the Elliptic Curve Digital Signature Algorithm (ECDSA) offers a variant of the Digital Signature Algorithm (DSA) which uses elliptic-curve cryptography. As with elliptic-curve cryptography in general, the bit size of the private key believed to be needed for ECDSA is about twice the size of the security level, in bits. (Source: Wikipedia), IoT33Internet of things (IoT): describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communication networks. (Source: Wikipedia) integration, and digital signatures. Preliminary modeling of the proposed system suggests potential improvements in operational efficiency and a reduction in data inaccuracies. The proposed system underscores the system's ability to streamline identity verification, improve data collection accuracy, and facilitate secure, confidential information sharing among DAC stakeholders. By enhancing the efficiency and reliability of DAC operations, this approach supports the scalable and effective deployment of NETs in the global effort to combat climate change. Future research will focus on empirical validation through pilot projects and simulations to further substantiate these claims.

On approximating partial scenario set cover

The Partial Scenario Set Cover problem (PSSC) generalizes the Partial Set Cover problem, which is itself a generalization of the classical Set Cover problem. We are given a finite ground set Q, a collection S of subsets of Q to choose from, each of which is associated with a nonnegative cost, and a second collection U of subsets of Q of which a given number l must be covered. The task is to choose a minimum cost sub-collection from S that covers at least l sets from U. PSSC is motivated by an application for locating emergency doctors.We present two approximation approaches. The first one combines LP-based rounding with a greedy consideration of the scenarios. The other is a variant of the greedy set cover algorithm, and in each iteration tries to minimize the ratio of cost to number of newly covered scenarios. We show that this subproblem, which we call Dense Scenario Set Cover (DSSC), is itself as hard to approximate as Set Cover and NP-hard, even when there is only a single scenario and all sets contain at most three elements. Furthermore, we consider a special case of DSSC where the sets are pairwise disjoint and show that in this case DSSC can be solved in polynomial time. We also provide an approximation for the general case, which we use as a subroutine in the greedy algorithm to obtain an approximation for PSSC.

Connected and Autonomous Vehicle Scheduling Problems: Some Models and Algorithms

In this paper, we consider some problems that arise in connected and autonomous vehicle (CAV) systems. Their simplified variants can be formulated as scheduling problems. Therefore, scheduling solution algorithms can be used as a part of solution algorithms for real-world problems. For four variants of such problems, mathematical models and solution algorithms are presented. In particular, three polynomial algorithms and a branch and bound algorithm are developed. These CAV scheduling problems are considered in the literature for the first time. More complicated NP-hard scheduling problems related to CAVs can be considered in the future.