Path Search Problem Research Articles

CodonBERT: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism.

Due to the varying delivery methods of mRNA vaccines, codon optimization plays a critical role in vaccine design to improve the stability and expression of proteins in specific tissues. Considering the many-to-one relationship between synonymous codons and amino acids, the number of mRNA sequences encoding the same amino acid sequence could be enormous. Finding stable and highly expressed mRNA sequences from the vast sequence space using in silico methods can generally be viewed as a path-search problem or a machine translation problem. However, current deep learning-based methods inspired by machine translation may have some limitations, such as recurrent neural networks, which have a weak ability to capture the long-term dependencies of codon preferences. We develop a BERT-based architecture that uses the cross-attention mechanism for codon optimization. In CodonBERT, the codon sequence is randomly masked with each codon serving as a key and a value. In the meantime, the amino acid sequence is used as the query. CodonBERT was trained on high-expression transcripts from Human Protein Atlas mixed with different proportions of high codon adaptation index codon sequences. The result showed that CodonBERT can effectively capture the long-term dependencies between codons and amino acids, suggesting that it can be used as a customized training framework for specific optimization targets. CodonBERT is freely available on https://github.com/FPPGroup/CodonBERT.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

Implementation of the A Star Heuristic Search Algorithm in Determining the Shortest Path

Finding the shortest path in a graph can be applied to various fields of shortest distance costs in routes, computer games, robotics or navigation. This study implements the A star heuristic search algorithm in determining the shortest path using the Visual Basic programming language and MySQL database. A star heuristic algorithm is implemented to find the shortest path between two vertices in a two-way weighted graph. We use a heuristic method to estimate the remaining cost from the start node to the destination point. This condition gives the A star algorithm the opportunity to choose the next closest node and optimize the search for the shortest route. Various experiments have been carried out with various graph conditions and with different complicated graphs to test the A star algorithm. The experimental results show that this algorithm succeeds in finding the shortest path efficiently in various situations. Our findings confirm that the A star algorithm has great potential in solving the shortest path search problem with an advantage in combining various information and remaining cost estimates, thereby minimizing the number of explored nodes and producing the shortest path efficiently.

Simulation of exchange routes on the Qinghai-Tibetan Plateau shows succession from the neolithic to the bronze age and strong control of the physical environment and production mode

The Qinghai-Tibetan Plateau (QTP) is essential for converging eastern, western, and northern prehistoric cultural spheres of Asia and Europe and for human adaptation to extreme environments. Reconstruction of the location and development of prehistoric exchange routes on the Qinghai-Tibetan Plateau underpins understanding human response to harsh environments and interaction and exchange between the three cultural spheres. This study simulates exchange routes for the Neolithic and Bronze Ages, using elevation, slope, vegetation, and rivers as cost data and site points as node data. A weighted network consisting of nodes and lines is constructed within the Qinghai-Tibetan Plateau using a weighted cumulative cyclic connectivity model among nodes-the simulation abstracts exchange routes as a path search problem on this weighted network. The final simulated route is the road with the lowest incremental cost. The results give a total length of Neolithic routes of about 16,900 km, with 15 main roads, and a total length of Bronze Age routes of approximately 16,300 km, with 18 main roads. Pathway development from the Neolithic to the Bronze Age shows an apparent successional relationship, with a spatial evolution from the marginal corridor to the marginal hinterland. The simulated routes overlap highly with archaeological evidence for transmission routes of corn and millet agriculture and wheat agriculture-domesticated animals-bronze metallurgy technology, indicating the reliability of the simulation results. Further analysis showed that the unique physical geography of the QTP constrained the formation and evolution of routes. River valleys were commonly chosen as routes to acclimatize people to the high, cold, and low oxygen levels of the Qinghai-Tibetan Plateau. Scattered small agricultural bases, established in areas of the QTP suitable for agricultural planting, are the basis for intersecting exchange routes. Road formation also reflects the clear differentiation in the agro-pastoral industry between high and low altitudes related to climate, ecological environment, and elevation. Interaction between agricultural and nomadic populations is the crucial motivation for forming and developing the exchange routes.

Bounded-Cost Bi-Objective Heuristic Search

There are many settings that extend the basic shortest path search problem. In Bounded-Cost Search, we are given a constant bound and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives) and the task is to minimize both objectives. In this paper, we combine both these settings into a new setting of Bounded-Cost Bi-Objective Search. We are given two bounds, one for each objective and the task is to find a solution within these bounds. We provide a scheme for normalizing the two objectives. We then introduce several algorithms for this new setting and compare them experimentally.

Modified A-Star (A*) Approach to Plan the Motion of a Quadrotor UAV in Three-Dimensional Obstacle-Cluttered Environment

Motion-planning algorithms play a vital role in attaining various levels of autonomy for any ground or flying agent. Three-dimensional (3D) motion-planning is interesting, but rather complex, especially for flying agents such as autonomous unmanned aerial vehicles (UAVs), due to the increased dimensionality of space and consideration of dynamical constraints for a feasible trajectory. Usually, in 3D path search problems, it is hard to avoid extra expanded nodes due to increased dimensionality with various available search options. Therefore, this paper discusses and implements a modified heuristic-based A* formalism that uses a truncation mechanism in order to eradicate the mentioned problem. In this formalism, the complete motion planning is divided into shortest path search problem and smooth trajectory generation. The shortest path search problem is subdivided into an initial naïve guess of the path and the truncation of the extra nodes. To find a naïve shortest path, a conventional two-dimensional (2D) A* algorithm is augmented for 3D space with six-sided search. This approach led to the inclusion of extra expanded nodes and, therefore, it is not the shortest one. Hence, a truncation algorithm is developed to further process this path in order to truncate the extra expanded nodes and then to shorten the path length. This new approach significantly reduces the path length and renders only those nodes that are obstacle-free. The latter is ensured using a collision detection algorithm during the truncation process. Subsequently, the nodes of this shortened path are used to generate a dynamically feasible and optimal trajectory for the quadrotor UAV. Optimal trajectory generation requires that the plant dynamics must be differentially flat. Therefore, the corresponding proof is presented to ensure generation of the optimal trajectory. This optimal trajectory minimizes the control effort and ensures longer endurance. Moreover, quadrotor model and controllers are derived as preliminaries, which are subsequently used to track the desired trajectory generated by the trajectory planner. Finally, results of numerical simulations which ultimately validate the theoretical developments are presented.

ECO routing based on network flow method

In this paper, a new ECO routing approach to the problem of resolving pin inaccessibility on standard cells is presented. The conventional ECO routing approaches conceptually divide the rerouting path search problem for a set of target nets to rip-up and reroute into two interdependent tasks: (i) (Off-cell routing) repairing the routing path between the net terminal outside of the cell and the cell boundary and (ii) (On-cell routing) repairing the routing path between the cell boundary and the pin on the cell where they have conventionally performed tasks (i) and (ii) iteratively for each target net. Yet, there is no easy way to accurately predict the final result of task (ii) for all nets during task (i). In our approach, we place the primary importance on resolving pin inaccessibility on cells. Consequently, we perform on-cell routing for all ripped-up nets simultaneously to resolve the pin inaccessibility first by utilizing flexible cell boundary access points and then to adjust the sub-paths outside of the cell. For a number of benchmark circuits, it is shown that our approach of early consideration of entire on-cell routing is very effective, resolving the pin inaccessibility of 71.09% out of all nets for which a commercial ECO router has failed to find legal routes.

Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model

The problem of minimal cost path search is especially difficult when no useful heuristics are available. A common solution is roll-out-based search like Monte Carlo Tree Search (MCTS). However, MCTS is mostly used in stochastic or adversarial environments, with the goal to identify an agent's best next move. For this reason, even though single player versions of MCTS exist, most algorithms, including UCT, are not directly tailored to classical minimal cost path search. We present Plackett-Luce MCTS (PL-MCTS), a path search algorithm based on a probabilistic model over the qualities of successor nodes. We empirically show that PL-MCTS is competitive and often superior to the state of the art.

SAT-based explicit [formula omitted] satisfiability checking

Linear Temporal Logic over finite traces (LTLf) was proposed in 2013 and has attracted increasing interest around the AI community. Though the theoretic basis for LTLf has been thoroughly explored since that time, there are still few algorithmic tools that are able to provide an efficient reasoning strategy for LTLf. In this paper, we present a SAT-based framework for LTLf satisfiability checking, which is the foundation of LTLf reasoning. We use propositional SAT-solving techniques to construct a transition system, which is an automata-style structure, for an input LTLf formula; satisfiability checking is then reduced to a path-search problem over this transition system. Based on this framework, we further present CDLSC (Conflict-Driven LTLf Satisfiability Checking), a novel algorithm (heuristic) that leverages information produced by propositional SAT solvers, utilizing both satisfiability and unsatisfiability results. More specifically, the satisfiable results of the SAT solver are used to create new states of the transition system and the unsatisfiable results to accelerate the path search over the system. We evaluate all 5 off-the-shelf LTLf satisfiability algorithms against our new approach CDLSC. Based on a comprehensive evaluation over 4 different LTLf benchmark suits with a total amount of 9317 formulas, our time-cost analysis shows that 1) CDLSC performs best on checking unsatisfiable formulas by achieving approximately a 4X time speedup, compared to the second-best solution (K-LIVE [1]); 2) Although no approaches dominate checking satisfiable formulas, CDLSC performs best on 2 of the total 4 tested satisfiable benchmark suits; and 3) CDLSC gains the best overall performance when considering both satisfiable and unsatisfiable instances.

Motion path planning of soccer training auxiliary robot based on genetic algorithm in fixed-point rotation environment

Soccer training assisted robot system is a combination of robotics and artificial intelligence. Motion path planning is an important part of soccer training assisted robot decision system. Path planning aims to find an optimal path, complete dynamic and static obstacle avoidance, and thus can timely and quickly carry out the path planning of the soccer training assisted robot cooperation strategy. Aiming at the problem of soccer training auxiliary robot motion path planning, this paper proposes a global path planning method based on genetic algorithm, which avoids the problem of unsmooth path and large computation. Under the premise of ensuring the completeness of search, this method transforms the soccer training assisted robot path planning problem into the path search problem of the soccer training assisted robot system center. Under the premise of using the fixed-point rotation method on a large scale, the genetic algorithm is adopted for the case of some special narrow-channel environments, and the action area is determined by simple search. Finally, the planning path of each soccer training-assisted robot in the system is obtained. Finally, the simulation results verify the effectiveness of the algorithm.

Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors.

We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats - each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic.

Analysis and Comparison of Monte Carlo Tree Search versus ACO Algorithms in Distribution of Resources and Environment

Swarm intelligence (CI) has been widely studied in the past decades. The most famous CI algorithm is ant colony algorithm (ACO), which is used to solve complex path search problems. AlphaZero program, through self-reinforcement learning of Monte Carlo tree algorithm from scratch, has achieved transcendental results in go, chess and general chess. By analyzing and comparing the Monte Carlo Tree Search (MCTS) and ACO algorithms on Distribution of resources and environment, the reasons for AlphaZero’s success are revealed. It is not only because of deep neural networks and intensive learning, but also because the algorithm is essentially an evolutionary algorithm of CI emergence.

SAT-Based Explicit LTLf Satisfiability Checking

We present a SAT-based framework for LTLf (Linear Temporal Logic on Finite Traces) satisfiability checking. We use propositional SAT-solving techniques to construct a transition system for the input LTLf formula; satisfiability checking is then reduced to a path-search problem over this transition system. Furthermore, we introduce CDLSC (Conflict-Driven LTLf Satisfiability Checking), a novel algorithm that leverages information produced by propositional SAT solvers from both satisfiability and unsatisfiability results. Experimental evaluations show that CDLSC outperforms all other existing approaches for LTLf satisfiability checking, by demonstrating an approximate four-fold speed-up compared to the second-best solver.

Investigating the Formation of Structural Elements in Proteins Using Local Sequence-Dependent Information and a Heuristic Search Algorithm.

Structural elements inserted in proteins are essential to define folding/unfolding mechanisms and partner recognition events governing signaling processes in living organisms. Here, we present an original approach to model the folding mechanism of these structural elements. Our approach is based on the exploitation of local, sequence-dependent structural information encoded in a database of three-residue fragments extracted from a large set of high-resolution experimentally determined protein structures. The computation of conformational transitions leading to the formation of the structural elements is formulated as a discrete path search problem using this database. To solve this problem, we propose a heuristically-guided depth-first search algorithm. The domain-dependent heuristic function aims at minimizing the length of the path in terms of angular distances, while maximizing the local density of the intermediate states, which is related to their probability of existence. We have applied the strategy to two small synthetic polypeptides mimicking two common structural motifs in proteins. The folding mechanisms extracted are very similar to those obtained when using traditional, computationally expensive approaches. These results show that the proposed approach, thanks to its simplicity and computational efficiency, is a promising research direction.

The Comparison of Three Algorithms in Shortest Path Issue

The shortest path problem is one of the most classical algorithm issues in graph theory, aiming to find the shortest path between the two nodes in a network. It is widely used to solving the path search problems in the relevant application, including network routing algorithm, artificial intelligence, game design, and so on. This paper introduces three basic algorithms and compares them theoretically by analyzing the time and space complexity. Then, the paper discusses their performances and summarizes the best application range. At last it introduces applications of shortest path algorithm.

A GIS-based analysis of constraints on pedestrian tsunami evacuation routes: Cascais case study (Portugal)

Tsunami hazard in coastal areas susceptible to flooding, although reduced (in terms of probability of occurrence), may pose a high risk. Therefore, in these areas, a detailed evacuation planning of the affected population is required as a risk mitigation measure. The knowledge and enforcement of evacuation routes may reduce the population vulnerability, making it more resilient and reducing risk. This paper presents a GIS approach for modelling evacuation routes based on the optimal path search problem, of the graph theory, which is implemented on ArcCasper tool. The methodology proposed considers the elements involved in the evacuation process, the worst credible tsunami inundation scenario (hazard extent and travel time), the number of people that needs to be evacuated in different time scenarios, the safe areas or destination points of the evacuation routes, the roads network characteristics and finally the time available to evacuate. The knowledge of those elements allows predicting some possible outcomes of the evacuation, such as the arrival time of the evacuees to a shelter and the identification of congestion hot spots resulting from the application of a flocking model which simulates the path to be used by evacuees avoiding obstacles. The municipality of Cascais was used to test the methodology proposed in this study. Cascais is one of the largest urban centres located about 25 km west of Lisbon, Portugal, with a high density of infrastructure along the coastline whereby most of the population and economic activities are exposed to a tsunami. The results, presented in the form of maps, allow identifying the optimal evacuation routes as well as the unfeasible routes. This crucial information could be used to the evacuation optimization regarding the location of meeting points and vertical shelters as well as to improve the accessibility of the areas to be evacuated.

A New Algorithm to Solve the Maximal Connected Subgraph Problem Based on Parallel Molecular Computing

DNA computing is widely used in complex NP-complete problems, such as the optimal portfolio problem, the optimum path search problem. DNA computing, having the characteristics of high parallelism, huge storage capacity and low energy loss, is very suitable for solving complex combinatorial problems. The maximal connected subgraph problem aims to find a connected vertex subset with maximal number of vertices in a simple undirected graph. Using biological computing technology, we give a new DNA algorithm to solve the maximal connected subgraphs problem with O(n) time complexity, which can significantly reduce the complexity of computing compared with the previous algorithms.

Premium-penalty ant colony optimization and its application in slope stability analysis

Ant colony optimization (ACO) is a very suitable path search algorithm, whose typical application is traveling salesman problem. However, as one heuristic algorithm, it has many shortcomings, such as slow convergent speed and low searching efficiency. To overcome these shortcomings, the premium-penalty strategy has been introduced, and the pheromone diversity of the good paths and the ordinary ones is increased to polarize pheromone density of all paths. Thus, premium-penalty ant colony optimization (PPACO) is proposed. And its good performance is verified by the applications to some typical traveling salesman problems. Its two important parameters are discussed too. Because location critical slip surface in slope stability analysis is a path search problem, it can be solved by the ACO very suitably. Therefore, based on PPACO and typical mature limit equilibrium analysis (Spencer method), a new method to analyze the slope stability is proposed. Through two typical examples, one simple slope and one complicated slope, the efficiency and effectiveness of the new algorithm are verified. The results show that, the new algorithm can always find the less safety factor and its critical slip surface in shorter time than many previous algorithms, and the new algorithm can be used in real engineering very well.

Solving the maximal matching problem with DNA molecules in Adleman–Lipton model

The maximal matching problem (MMP) is to find maximal edge subsets in a given undirected graph, that no pair of edges are adjacent in the subsets. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications in optimal combination and linear programming fields. It can be difficultly solved by the electronic computer in exponential level time. Meanwhile in previous studies deoxyribonucleic acid (DNA) molecular operations usually were used to solve NP-complete continuous path search problems, e.g. HPP, traveling salesman problem, rarely for NP-hard problems with discrete vertices or edges solutions, such as the minimum vertex cover problem, graph coloring problem and so on. In this paper, we present a DNA algorithm for solving the MMP with DNA molecular operations. For an undirected graph with [Formula: see text] vertices and [Formula: see text] edges, we reasonably design fixed length DNA strands representing vertices and edges of the graph, take appropriate steps and get the solutions of the MMP in proper length range using [Formula: see text] time. We extend the application of DNA molecular operations and simultaneously simplify the complexity of the computation.

A Hybrid Multicast Routing for Large Scale Sensor Networks with Holes

In this article, we present RE $^2$ MR, the first hybrid multicast routing protocol that builds on the strengths of existing topology-based, hierarchical and geographic multicast solutions, while addressing their limitations. In RE $^2$ MR, the multicast path search problem is formulated as the capacitated concentrator location problem (CCLP) which yields the network topology that minimizes the sum of path lengths from the multicast root to multicast members. Furthermore, its trajectory-based lightweight hole detection (THLD) discovers deployment area irregularities (i.e., network holes) that affect its solution and autonomously take them into account to generate updated routing paths, and its Energy-efficient Packet Forwarding (EPF) and Multi-level Facility Computation (MFC) reduce computational and communication overheads. We implement RE $^2$ MR in TinyOS and evaluate it extensively using TOSSIM for relatively large-scale simulations (400 nodes); we also implement RE $^2$ MR on real-hardware and perform experiments on a testbed consisting of 42 TelosB motes. Through the simulations and experiments on real-hardware, we demonstrate that RE $^2$ MR reduces the energy consumption by up to 57 percent and the end-to-end delay by up to 8 percent, when compared with the state-of-the-art multicast routing protocols.