Solution Path Research Articles

Hierarchical and smoothed topographic path planning for large-scale virtual simulation environments

Virtual simulation systems ought to make the most of agents’ realistic behaviors so that users feel immersed in virtual scenarios that mimic the real-world environment. Usually used for training and instruction purposes, agent-based simulations have additional requirements to be considered by the Artificial Intelligence solutions often explored in the development of computer games. To provide a valuable impression of how the simulated agents navigate in the real-world terrain, one of the key requirements indicates that topographic terrain characteristics need to be considered in the path planning tasks. It means that important aspects of the agents’ navigation capabilities have to be properly handled by the used pathfinding algorithms. This problem is even more challenging when large-scale terrain scenarios are considered in state-of-the-art simulation systems as the path planning may be too time-consuming when handling terrain representations with large search spaces. To approach the challenge of computing safe and realistic path solutions for agents inserted in simulation-based learning environments, this work discusses a hierarchical pathfinding algorithm named HPATheta*, showing how to compute terrain relief-aware smoothed paths in reasonable computing time when dealing with path planning in large-scale real-world virtual terrains. Using different simulated real-world virtual terrains, the proposal is validated by performing an extensive experimental campaign with the use of large-scale virtual terrain representations. The experimental results indicate that the proposed approach is able to efficiently address the targeted problem, showing better results compared to other similar path planning algorithms.

Reliable trajectory-adaptive routing strategies in stochastic, time-varying networks with generalized correlations

This paper focuses on the problem of finding optimal trajectory-adaptive routing strategies in stochastic time-varying networks with generalized spatio-temporal correlations. A representation for jointly distributed continuous link travel times across the entire network with time-varying distributions and correlation structures is presented, and the crucial characteristics and methodological difficulties of the problem are discussed. The paper presents a generalized 2-stage path and strategy finding solution approach that can serve for finding both exact and approximate solutions with the tuning of a risk-level tolerance parameter. The first stage of the solution approach generates eligible paths, where the risk-level parameter is used to eliminate paths that are likely to be inefficient. The second stage finds reliable trajectory-adaptive strategies, using the eligible paths only, based on one or multiple reliability-based optimality conditions. Thus, the approach allows the user to determine the optimal strategy for one or multiple groups of travelers with different reliability preferences. Numerical experiments show that the average running time of the algorithm reduces super-linearly with the increase of the risk-tolerance parameter ∊, while incurring some loss to the objective function relative to the exact solution. Thus, the heuristic can offer significant benefits in reducing the run time of the solution algorithm, while finding adaptive strategy solutions that consistently maintain better objective function values compared to the a priori (i.e., non-adaptive) solutions.

Performance Improvement of KCS (Kalina Cycle System) 34 by Replacing Throttle Valve With Single-Screw Expander

In order to recover the energy loss due to the throttling in the path of ammonia-lean solution in the Kalina Cycle System (KCS) 34, two redesigned cycles, in which single-screw expanders that can perform two-phase expansion are used to replace the throttle valve, are proposed in this paper. The results show that the thermal efficiency and net work of two redesigned cycles are higher than those of the original KCS 34. With the concentration increase of ammonia-water mixture, the work produced by the single-screw expander B in two redesigned cycles gradually decreases, and the difference between the work produced in two redesigned cycles also gradually decreases. The original KCS 34 and two redesigned cycles have high exergy efficiency. The highest cycle exergy efficiency of 56.59% can be obtained in the II-redesigned cycle when the evaporation pressure is 3.0 MPa and ammonia-water concentration is 0.75.

Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO

This paper studies a two-stage model for the optimization of charging scheduling and charging station construction planning based on the spatial-temporal complementarity of charging demand, in order to improve the overall satisfaction of the whole society. In the first stage, starting from the probability of trip starting point and other characteristics, the simulation of load demand is realized based on Monte Carlo simulation, and the charging plan regulation method is created combined with the principle of Dijkstra algorithm. In the second stage, a solution path based on discrete binary particle swarm optimization is proposed. The experimental results of 14 scenarios show that no matter disorderly charging or orderly charging, the annual social comprehensive cost of the scenario with five charging stations is the lowest. Compared with disordered charging, the total construction capacity of charging station is reduced by 51.61%, and the annual social comprehensive cost is reduced by 29.35%. Therefore, this study provides a framework for the optimal configuration of charging stations, which is applicable when considering or not considering demand response, and provides a solution for the future green development planning of urban charging infrastructure.

Advances in Neuromorphic Spin-Based Spiking Neural Networks: A review

This article reviews the recent developments and challenges in spintronic based spiking neural networks (SNNs). The present CPUs and GPUs are powerful tools that are capable of computing complex artificial intelligence (AI) tasks, yet they face several challenges. The systems utilizing machine learning techniques require extensive computations including vector-matrix multiplication. The traditional von Neumann architecture is inefficient in the hardware implementation of such systems. Neuromorphic architectures are computationally more efficient that ushers the era of high-performance hardware for AI applications. This also caters to the need of hardware solutions for biologically inspired in-memory computing functionality using nonvolatile memory (NVM). These biologically inspired networks have attracted a great attention due to their inherent computational efficiency in performing pattern/speech recognition and image classification tasks. During the last decade, SNNs have emerged as one of the suitable solutions for imitating the biological brain. Information is temporarily encoded in SNNs and the spikes serve as a mode of communication between the neurons. Various NVM devices such as spin-based memories, resistive randomaccess memory (RRAM), phase change memory (PCM), and conductive-bridging RAM (CBRAM) have been explored for the hardware implementation of NNs. Spin-based memory devices demonstrate much more energy efficiency while providing superior information processing capabilities than other NVMs. These devices also provide high endurance, scalability, CMOS compatibility, and ultralow power consumption characteristics. The spiking functionality in neurons can be easily mapped to various spin devices with synaptic learning, therefore, paving the path for future AI hardware solutions.

A new path planning method based on sparse A* algorithm with map segmentation

Due to the complexity of map modeling, the massive computation and high redundancy of the traditional A* algorithm will greatly reduce the efficiency of pathfinding, resulting in huge performance consumption. Meanwhile, limited by neighborhood search strategy in grid map, the traditional A* algorithm is actually unable to achieve the optimal path in the global sense. To solve these problems, this paper proposes an improved A* algorithm based on graph preprocessing. First, the free space on the map was decomposed into several polygon regions using the improved convex decomposition method based on Maklink. Then, each region was coded into feature nodes according to A* algorithm. Finally, an optimal region passage was found based on the principle of A* algorithm, in which the global optimal path solution was obtained. Compared with the traditional A* algorithm and other classical path planning algorithms, the proposed algorithm has significant advantages in planning speed, path cost, stability, and completeness.

Success or Not? – Bicycle Sharing in London

Since the birth of shared bicycles, it has become the main means of transportation for residents to make green trips. However, many problems have also arisen, such as the limitation of stop points, excessive occupation of public land, frequent damage and theft, etc., which restrict the contribution of bicycles and development. Based on this, this article mainly takes Santander Bicycle Company as an example to conduct a case study and explore the constraints from the perspective of stakeholders to find the optimal solution path.

Towards Ecological Sustainability: Assessing Dynamic Total-Factor Ecology Efficiency in Africa.

Ecological footprint (EF) and human development index (HDI) are two critical indicators for assessing sustainable development worldwide. Past studies in Africa have ignored dynamic sustainable total-factor ecological efficiency (DSTFEE) assessment. This present study proffers a novel dynamic sustainable total-factor ecological efficiency (DSTFEE) that comprehensively assesses the ecological efficiency among 44 sampled African economies from 2010 to 2016. Our study incorporates EF and HDI in the model. Second, the study evaluates regional DSTFEE heterogeneity efficiency as well as the technological gap efficiency in Africa. Further, projection analysis is done to offer a viable solution path to address the inefficient African countries. Third, the study investigates the determinants of ecological efficiency using the bootstrap truncation regression technique. The results from the implemented models are as follows: first, the DSTFEE for the 44 sampled African countries is very low (0.403), indicating enormous potential for improvement. Second, the heterogeneity of DSTFEE across the five Africa regional blocs is evident. The southern bloc had the highest efficiency score, followed by the northern, central, western, and eastern regions. The technology gap ratio also reveals a massive gap among the five Africa regional blocs. Third, the bootstrap truncation regression results established a U-shape nexus between growth and DSTFEE in Africa. REC and trade openness is positively corrected to DSTFEE for African countries. In contrast, financial development, foreign direct investment (FDI), and urbanization impede dynamic ecological efficiency in Africa. The study’s results equip African countries with adequate knowledge of their ecological efficiency situation and provide them a viable path to improve environmental efficiency, thereby boosting their ecological sustainability.

Random‐Objective Waveform Inversion of 3D‐9C Shallow‐Seismic Field Data

AbstractRobustness and uncertainty estimation are two challenging topics in full‐waveform inversion (FWI). To overcome these challenges, we present the methodology of random‐objective waveform inversion (ROWI), which adopts a multi‐objective framework and a preconditioned stochastic gradient descent optimization algorithm. The use of one shot per iteration avoids using redundant data and reduces the computational cost. The Pareto solutions represent a group of most likely solutions and their differences quantifies the model uncertainty associated with the trade‐off between conflicting objective functions. Due to the high dimensionality in the data and model spaces, it is prohibitively expensive to check the Pareto optimality of all solutions explicitly. Thus, we decompose the original multi‐objective function into shot‐related subproblems and use the Pareto solutions of the subproblems for trade‐off analysis. We apply ROWI to a field multi‐component shallow‐seismic data set acquired in Rheinstetten, Germany. The 3D near‐surface model is successfully reconstructed by ROWI and the main target, a refilled trench, is delineated. We compare the results estimated by ROWI and a conventional least squares FWI to prove the high efficiency of ROWI. We run six ROWI tests on the field data with different solution paths to prove the robustness of ROWI against the random solution path. The validity of the reconstructed model is verified by multiple 2D ground‐penetrating radar profiles. We estimate 246 Pareto solutions of multi‐objective subproblems for trade‐off analysis. Another four ROWI tests starting from different poor initial models are performed, whose results prove the relatively high robustness of ROWI against the initial model.

Ranking Conjunctions for Partial Delete Relaxation Heuristics in Planning

Heuristic search is one of the most successful approaches to classical planning, finding solution paths in large state spaces. A major focus has been the development of domain-independent heuristic functions. One recent method are partial delete relaxation heuristics, improving over the standard delete relaxation heuristic through imposing a set C of conjunctions to be treated as atomic. Practical methods for selecting C are based on counter-example guided abstraction refinement, where iteratively a relaxed plan is checked for conflicts and new atomic conjunctions are introduced to address these. However, in each refinement step, the choice of possible new conjunctions is huge. The literature so far offers merely one simple strategy to make that choice. Here we fill that gap, considering a sizable space of basic ranking strategies as well as combinations thereof. We furthermore devise ranking strategies for conjunction-forgetting, where the ranking pertains to the current conjunctions and thus statistics over their usefulness can be maintained. Our experiments show that ranking strategies do make a large difference in performance, and that our new strategies can be useful.

Solving the Shortest Path of Urban Traffic Network Based on MapReduce

Aiming at the problem of huge amount of information data for solving the shortest path of urban traffic network, based on the analysis of the characteristics and defects of genetic algorithm, a parallel genetic algorithm based on MapReduce is proposed to solve the shortest path of urban traffic network. By designing the adaptive function, the algorithm can more effectively solve the shortest path problem and effectively avoid local optimization, improve the running speed and convergence of the algorithm. The experimental results show that the parallel genetic algorithm based on MapReduce is an effective shortest path solution method, which is efficient, effective and feasible.

Traveling waves in a mean field learning model

Lucas and Moll have proposed in [] a system of forward–backward partial differential equations that model knowledge diffusion and economic growth. It arises from a microscopic model of learning for a mean-field type interacting system of individual agents. In this paper, we prove existence of traveling wave solutions to this system. They correspond to what is known in economics as balanced growth path solutions. We also study the dependence of the solutions and their propagation speed on various economic parameters of the system.

Predicting Optimal Solution Cost with Bidirectional Stratified Sampling (Abstract)

Optimal planning and heuristic search systems solve state-space searchproblems by finding a least-cost path from start to goal. As a byproduct of having an optimal path they also determine the optimal solution cost. In this paper we focus on the problem of determining the optimal solution cost for a state-space search problem directly, i.e., without actually finding a solution path of that cost. We present an efficient algorithm, BiSS, based on ideas of bidirectional search and stratified sampling that produces accurate estimates of the optimal solution cost. Our method is guaranteed to return the optimal solution cost in the limit as the sample size goes to infinity.

A deep neural network with a fuzzy multi-objective optimization model for fault analysis in an elastic optical network

The elastic optical network (EON) is the most attractive architecture for the next generation of optical networks. Dealing with high bit-rate traffic, EON faces the challenge of ensuring survivability to operate with stringent Service Level Agreements. This article proposes a Deep Neural Network model with a multi-objective Fuzzy Inference System (FIS) to solve the Routing and Spectrum Assignment problem with Shared Backup Path Protection. The algorithm aims to optimize the trade-off between blocking probability (BP) and fault restoration ratio (FRR). It uses a new spectrum-fragmentation metric to improve the FRR of affected connections. The FIS adds features of load balancing and alignment of allocation path solutions. We use figures of merit as BP of connection requests, FRR, spectrum utilization ratio, and connection downtime to evaluate the algorithm performance. The proposed algorithm organizes traffic in a less fragmented way, efficiently uses routing and protection resources, and performs well compared to similar algorithms related in the literature.

Particle swarm optimization for the shortest path problem

Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: road-damage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. Particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations.

Unified distributed robust regression and variable selection framework for massive data

This paper proposes a unified distributed robust regression framework for distributed massive data, which can include many robust regressions in one setting. Specifically, we first transfer different types of robust regressions into an asymptotically equivalent least-squares problem. Then the resulting estimator can be calculated as a weighted average of robust local estimators, and the communication cost is reduced, since it involves only one round of communication. In addition, since the local data information is incorporated sufficiently, it is adaptive to the heterogeneity. The new estimator is proven to be equivalent with the corresponding global robust regression estimator. Furthermore, we conduct variable selection based on the unified robust regression framework and adaptive LASSO, and the path of solution can also be conveniently obtained by LARS algorithm. It is theoretically shown that the new variable selection method can select true relevant variables consistently by using a new distributed BIC-type tuning parameter selector. The simulation results confirm the effectiveness of the new methods and the correctness of the theoretical results.

Smart meter data classification using optimized random forest algorithm

Implementing a proper clustering algorithm and a high accuracy classifier for applying on electricity smart meter data is the first stage in analyzing and managing electricity consumption. In this paper, Random Forest (RF) classifier optimized by Artificial Bee Colony (ABC) which is called Artificial Bee Colony-based Random Forest (ABC-RF) is proposed. Also, in order to determine the representative load curves, the Convex Clustering (CC) is used. The solution paths generated by convex clustering show relationships among clusters that were hidden by static methods such as k-means clustering. To validate the proposed method, a case study that includes a real dataset of residential smart meters is implemented. The results evidence that the proposed ABC-RF method provides a higher accuracy if compared to other classification methods.

An Analysis of the Influences of a Hybrid Learning Environment in the Solution of Vector Tasks according to the Anthropological Theory of the Didactic (ATD)

This paper discusses a part of a doctoral study based on the theoretical pillars of the Anthropological Theory of the Didactic (ATD). Supported by this theory, our research interest followed a path that led us to the teaching of the mathematical object vector and its institutional configuration in a Mathematics Teaching course at Universidade Estadual de Feira de Santana (UEFS), in the state of Bahia, Brazil, centered on two dichotomous aspects. The first one refers to the possibility of application and institutional relevance of this mathematical object. The second aspect is related to the obstacles encountered in the didactical scope, which have impact on the teaching/learning of this mathematical object. In this discussion, the question that guided our investigation emerged: how to harness praxeological recombinations that can promote mediation between personal and institutional relationships in the scope of vector knowledge in the Mathematics Teaching course at UEFS? Based on this context, we aim at analyzing students’ productions in terms of solution paths to a vector task in a Hybrid Learning Environment (HLE), from a developmental perspective based on the T4TEL didactical framework (Chaachoua, 2018). With regard to methodological support, Didactical Engineering provided directions that enabled us to make a comparison between a priori and a posteriori analyses, resulting in the identification of the reach of the techniques that were developed in contrast to the naturalized techniques that are part of this context.

AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow

Principal component analysis (PCA) has been widely used as an effective technique for feature extraction and dimension reduction. In the High Dimension Low Sample Size setting, one may prefer modified principal components, with penalized loadings, and automated penalty selection by implementing model selection among these different models with varying penalties. The earlier work (Zou et al. in J Comput Graph Stat 15(2):265–286, 2006; Gaynanova et al. in J Comput Graph Stat 26(2):379–387, 2017) has proposed penalized PCA, indicating the feasibility of model selection in \(\ell _2\)-penalized PCA through the solution path of Ridge regression, however, it is extremely time-consuming because of the intensive calculation of matrix inverse. In this paper, we propose a fast model selection method for penalized PCA, named approximated gradient flow (AgFlow), which lowers the computation complexity through incorporating the implicit regularization effect introduced by (stochastic) gradient flow (Ali et al. in: The 22nd international conference on artificial intelligence and statistics, pp 1370–1378, 2019; Ali et al. in: International conference on machine learning, 2020) and obtains the complete solution path of \(\ell _2\)-penalized PCA under varying \(\ell _2\)-regularization. We perform extensive experiments on real-world datasets. AgFlow outperforms existing methods (Oja and Karhunen in J Math Anal Appl 106(1):69–84, 1985; Hardt and Price in: Advances in neural information processing systems, pp 2861–2869, 2014; Shamir in: International conference on machine learning, pp 144–152, PMLR, 2015; and the vanilla Ridge estimators) in terms of computation costs.

Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling

In large and complex project schedule networks, existing algorithms to determine the critical path are considerably slow. Therefore, an algorithm with a faster convergence is needed to improve the efficiency of the critical path computation. The ant colony algorithm was first applied to the travelling salesman problem to determine the shortest path. However, many problems require the longest path in practice; the critical path in the scheduling problem is the longest path in the scheduling network. In this study, an improved ant colony algorithm to determine the critical path by setting the path distance and time as negative, while the transition probability remains unchanged, is proposed. The case of a coal power plant engineering, procurement, and construction (EPC) project was considered. The results show that a peak number of optimal solutions appeared at approximately the 9th iteration; however, instabilities and continued fluctuations were observed even afterward, indicating that the algorithm has a certain randomness. Convergence is apparent at the 29th iteration; after the 34th iteration, a singular optimal solution, the longest or critical path, is obtained, indicating that the convergence rate can be controlled and that the critical path can be obtained by setting appropriate parameters in the solution method. This has been found to improve the efficiency of calculating the critical path. Case validation and algorithm performance testing confirmed that the improved ant colony algorithm can determine the critical path problem and make it computationally intelligent.