scholarly journals Heuristic Search When Time Matters

2013 ◽  
Vol 47 ◽  
pp. 697-740 ◽  
Author(s):  
E. Burns ◽  
W. Ruml ◽  
M. B. Do
Keyword(s):  
Heuristic Search ◽  
Time Pressure ◽  
Search Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Utility Functions ◽  
Training Data ◽  
Current State ◽  
Shortest Path Algorithms

In many applications of shortest-path algorithms, it is impractical to find a provably optimal solution; one can only hope to achieve an appropriate balance between search time and solution cost that respects the user's preferences. Preferences come in many forms; we consider utility functions that linearly trade-off search time and solution cost. Many natural utility functions can be expressed in this form. For example, when solution cost represents the makespan of a plan, equally weighting search time and plan makespan minimizes the time from the arrival of a goal until it is achieved. Current state-of-the-art approaches to optimizing utility functions rely on anytime algorithms, and the use of extensive training data to compute a termination policy. We propose a more direct approach, called Bugsy, that incorporates the utility function directly into the search, obviating the need for a separate termination policy. We describe a new method based on off-line parameter tuning and a novel benchmark domain for planning under time pressure based on platform-style video games. We then present what we believe to be the first empirical study of applying anytime monitoring to heuristic search, and we compare it with our proposals. Our results suggest that the parameter tuning technique can give the best performance if a representative set of training instances is available. If not, then Bugsy is the algorithm of choice, as it performs well and does not require any off-line training. This work extends the tradition of research on metareasoning for search by illustrating the benefits of embedding lightweight reasoning about time into the search algorithm itself.

scholarly journals Anytime Heuristic Search

2007 ◽  
Vol 28 ◽  
pp. 267-297 ◽  
Author(s):  
E. A. Hansen ◽  
R. Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Search Problem ◽  
A Algorithm ◽  
Solution Quality ◽  
Multiple Sequence ◽  
Anytime Algorithm ◽  
Heuristic Search Algorithm

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

scholarly journals Frontier Search and Plan Reconstruction in Oversubscription Planning

2019 ◽  
Vol 33 ◽  
pp. 9999-10000
Author(s):  
Daniel Muller
Keyword(s):  
Recent Work ◽  
Heuristic Search ◽  
Reference Point ◽  
Search Algorithm ◽  
Utility Functions ◽  
Action Sequence ◽  
New Information ◽  
New Knowledge ◽  
Total Action ◽  
High Utility

Oversubscription planning (OSP) is the problem of choosing an action sequence which reaches a state with a high utility, given a budget for total action cost. This formulation allows us to handle situations with under-constrained resources, which do not allow us to achieve all possible goal facts. In optimal OSP, the task is further constrained to finding a path which achieves a state with maximal utility. An incremental BFBB search algorithm with landmark-based approximations, proposed for OSP heuristic search to address tasks with non-negative and 0-binary utility functions. Incremental BFBB maintained with the best solution so far and a set of reference states, extended with all the non-redundant value-carrying states discovered during the search. Each iteration requires search re-start in order to exploit the new knowledge obtained along the search. Recent work proposed an approach of relative estimation of achievements with value-driven landmarks to address arbitrary utility functions, which incrementally improves the best existing solution so far eliminating the need to maintain a set of reference states. We now propose a progressive frontier search algorithm, which alleviates the need to re-start from scratch once new information is acquired by capturing the frontier achieved at the end of each iteration which is used as a dynamic reference point to continue the search, leading to improved efficiency of the search.

scholarly journals Heuristic Search for Approximating One Matrix in Terms of Another Matrix

2021 ◽  
Author(s):  
Guihong Wan ◽  
Haim Schweitzer
Keyword(s):  
Heuristic Search ◽  
Optimal Algorithm ◽  
Greedy Algorithms ◽  
Optimal Solution ◽  
Approximation Problem ◽  
Optimal Algorithms ◽  
A Algorithm ◽  
Current State ◽  
Target Matrix ◽  
Selection Algorithms

We study the approximation of a target matrix in terms of several selected columns of another matrix, sometimes called "a dictionary". This approximation problem arises in various domains, such as signal processing, computer vision, and machine learning. An optimal column selection algorithm for the special case where the target matrix has only one column is known since the 1970's, but most previously proposed column selection algorithms for the general case are greedy. We propose the first nontrivial optimal algorithm for the general case, using a heuristic search setting similar to the classical A* algorithm. We also propose practical sub-optimal algorithms in a setting similar to the classical Weighted A* algorithm. Experimental results show that our sub-optimal algorithms compare favorably with the current state-of-the-art greedy algorithms. They also provide bounds on how close their solutions are to the optimal solution.

scholarly journals Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

2019 ◽  
Vol 33 ◽  
pp. 2280-2287 ◽  
Author(s):  
Baokun He ◽  
Swair Shah ◽  
Crystal Maung ◽  
Gordon Arnold ◽  
Guihong Wan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Heuristic Search ◽  
Search Algorithm ◽  
A Priori ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Combinatorial Search ◽  
Heuristic Search Algorithm

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.

scholarly journals Two New Beetle Antennae Search (BAS) Algorithms and Their Comparative Investigation

2018 ◽  
Vol 2 (1) ◽  
pp. 9 ◽  
Author(s):  
Zongyuan Lin ◽  
Sile Ma ◽  
Xiaojing Ma ◽  
Xiangyuan Jiang ◽  
Shuai Li
Keyword(s):  
Heuristic Search ◽  
Search Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Comparative Investigation ◽  
Test Results ◽  
Heuristic Search Algorithm ◽  
Fitness Value ◽  
Stability And Accuracy ◽  
Environment Parameters

The Beetle Antennae Search (BAS) algorithm is a meta-heuristic search algorithm, which has efficient search capabilities. This paper presents two different variant algorithms based on the BAS algorithm, which are the BAS with fitness value (BASF) algorithm and BAS with local fast search (BASL) algorithm. The test results of 23 benchmark functions will be used to verify the reliability and accuracy of these algorithm. These benchmark functions include unimodal and multimodal high-dimensional functions, as well as fixed-dimensional multimodal functions. The test results show that the improved algorithm can search for the optimal solution globally and accurately with its own search strategy without environment parameters. Stability and accuracy are significantly improved while the calculation time does not change much.

scholarly journals Improved Sparrow Search Algorithm Based on Iterative Local Search

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Shaoqiang Yan ◽  
Ping Yang ◽  
Donglin Zhu ◽  
Wanli Zheng ◽  
Fengxuan Wu
Keyword(s):  
Local Search ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Search Performance ◽  
Search Range ◽  
Variable Helix ◽  
Local Optimal Solution ◽  
Local Search Strategy

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

scholarly journals Exploiting Obstacle Geometry to Reduce Search Time in Grid-Based Pathfinding

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1186
Author(s):  
Fahed Jubair ◽  
Mohammed Hawa
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Time ◽  
Search Tree ◽  
Search Algorithms ◽  
Binary Search ◽  
Binary Search Tree ◽  
Grid Map ◽  
Heuristic Search Algorithms ◽  
The Impact

Pathfinding is the problem of finding the shortest path between a pair of nodes in a graph. In the context of uniform-cost undirected grid maps, heuristic search algorithms, such as A ★ and weighted A ★ ( W A ★ ), have been dominantly used for pathfinding. However, the lack of knowledge about obstacle shapes in a gird map often leads heuristic search algorithms to unnecessarily explore areas where a viable path is not available. We refer to such areas in a grid map as blocked areas (BAs). This paper introduces a preprocessing algorithm that analyzes the geometry of obstacles in a grid map and stores knowledge about blocked areas in a memory-efficient balanced binary search tree data structure. During actual pathfinding, a search algorithm accesses the binary search tree to identify blocked areas in a grid map and therefore avoid exploring them. As a result, the search time is significantly reduced. The scope of the paper covers maps in which obstacles are represented as horizontal and vertical line-segments. The impact of using the blocked area knowledge during pathfinding in A ★ and W A ★ is evaluated using publicly available benchmark set, consisting of sixty grid maps of mazes and rooms. In mazes, the search time for both A ★ and W A ★ is reduced by 28 % , on average. In rooms, the search time for both A ★ and W A ★ is reduced by 30 % , on average. This is achieved while preserving the search optimality of A ★ and the search sub-optimality of W A ★ .

scholarly journals Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

2017 ◽  
Author(s):  
Jingwei Chen ◽  
Robert C. Holte ◽  
Sandra Zilles ◽  
Nathan R. Sturtevant
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Worst Case ◽  
Heuristic Search Algorithm ◽  
Bidirectional Search ◽  
Optimal Node ◽  
Minimum Number ◽  
Better Than

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

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