Anytime pack search

Vadlamudi, Satya Gautam; Aine, Sandip; Chakrabarti, P. P.

doi:10.1007/s11047-015-9490-9

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…In our experiments we therefore considered such difficult skewed instances as well as easier balanced instances and studied the impact of diverse parameters like the different lower bounds, the beam width, and the usage of the LS. Furthermore we compared our approach to the anytime A * variants APS from [32] and ARA * from [14], the pure A * algorithm and a basic CP model solved by the ILOG CP solver. In particular for large instances A * +BS+LS significantly outperforms the ILOG CP approach and in almost all cases where the ILOG CP solver provides smaller average optimality gaps our approach is able to solve more instances to optimality.…”

Section: Discussionmentioning

confidence: 99%

“…Anytime Pack Search (APS), introduced by Vadlamudi et al [32], is based on BS. The algorithm consecutively performs BS iterations, keeping partial solutions which are pruned during the BS iterations in memory.…”

Section: Related Workmentioning

confidence: 99%

“…As described in Section 2, Anytime Pack Search (APS) from Vadlamudi et al [32] is an approach similar to our BS-enhanced A * algorithm and based on consecutive BS iterations. In APS terminology the beam width k bw is called pack size.…”

Section: Comparison To Anytime Pack Searchmentioning

confidence: 99%

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Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Horn

Raidl

Blum

2019

Artificial Intelligence

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We consider a sequencing problem that arises, for example, in the context of scheduling patients in particle therapy facilities for cancer treatment. A set of non-preemptive jobs needs to be scheduled, where each job requires two resources: (1) a common resource that is shared by all jobs and (2) a secondary resource, which is shared with only a subset of the other jobs. While the common resource is only required for a part of the job's processing time, the secondary resource is required for the whole duration. The objective is to minimize the makespan. First we show that the tackled problem is NP-hard and provide three different lower bounds for the makespan. These lower bounds are then exploited in a greedy construction heuristic and a novel exact anytime A * algorithm, which uses an advanced diving mechanism based on Beam Search and Local Search to find good heuristic solutions early. For comparison we also provide a basic Constraint Programming model solved with the ILOG CP optimizer. An extensive experimental evaluation on two types of problem instances shows that the approach works even for large instances with up to 2000 jobs extremely well. It typically yields either optimal solutions or solutions with an optimality gap of less than 1%.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Horn

Raidl

Blum

2019

Artificial Intelligence

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“…These constraints are iteratively relaxed to improve the solution quality in an anytime manner. These algorithms include variants of depth-first search (Depth-first Branch-and-Bound search [52] and Complete Anytime Beam search [53]), variants of the beam search [54] (Beam-Stack search [55], ABULB [56,57], Anytime Pack Search [58], Anytime Column Search [59]), Anytime Window A* (AWinA*) [60], where a window of chosen depth is used to restrict the active search space, Anytime Explicit Estimation Search (AEES) [61], that uses a distance-to-go estimate (similar to the A* algorithm [62]) to determine the order of expansions. Unlike WA* based approaches, most of these algorithms do not provide any implicit bounds on their solutions.…”

Section: Anytime Search Algorithmsmentioning

confidence: 99%

Truncated incremental search

Aine

Likhachev

2016

Artificial Intelligence

Self Cite

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Incremental heuristic search algorithms reuse their previous search efforts whenever these are available. As a result, they can often solve a sequence of similar planning problems faster than planning from scratch. State-of-the-art incremental heuristic searches (such as LPA*, D* and D* Lite) work by propagating cost changes to all the states in the search tree whose g values (the costs of computed paths from the start state) are no longer optimal. This work is based on the observation that while a complete propagation of cost changes is essential to ensure optimality, the propagations can be stopped earlier if we are looking close-to-optimal solutions instead of the optimal one. We develop a framework called Truncated Incremental Search that builds on this observation and uses a target suboptimality bound to efficiently restrict cost propagations. We present two truncation based algorithms, Truncated LPA* (TLPA*) and Truncated D* Lite (TD* Lite), for bounded suboptimal planning and navigation in dynamic graphs. We also develop an anytime replanning algorithm, Anytime Truncated D* (ATD*), that combines the inflated heuristic search with truncation, in an anytime manner. We discuss the theoretical properties of these algorithms proving their correctness and efficiency, and present experimental results on 2D and 3D (x, y, heading) path planning domains evaluating their performance. The empirical results show that the truncated incremental searches can provide significant improvement in runtime over existing incremental search algorithms, especially when searching for close-to-optimal solutions in large, dynamic graphs.

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“…In this approach, classical A * search iterations are intertwined with iterations of Anytime column search [25]. This hybrid was able to outperform some other state-of-the-art anytime algorithms from the literature, such as PRO_MLCS [26] and Anytime Pack Search [27], in terms of solution quality. To the best of our knowledge, this hybrid approach is still the leading method for the LCS problem on a wide range of benchmark sets from the literature.…”

Section: Introductionmentioning

confidence: 99%

Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

et al. 2021

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The longest common subsequence (LCS) problem is a prominent NP–hard optimization problem where, given an arbitrary set of input strings, the aim is to find a longest subsequence, which is common to all input strings. This problem has a variety of applications in bioinformatics, molecular biology and file plagiarism checking, among others. All previous approaches from the literature are dedicated to solving LCS instances sampled from uniform or near-to-uniform probability distributions of letters in the input strings. In this paper, we introduce an approach that is able to effectively deal with more general cases, where the occurrence of letters in the input strings follows a non-uniform distribution such as a multinomial distribution. The proposed approach makes use of a time-restricted beam search, guided by a novel heuristic named Gmpsum. This heuristic combines two complementary scoring functions in the form of a convex combination. Furthermore, apart from the close-to-uniform benchmark sets from the related literature, we introduce three new benchmark sets that differ in terms of their statistical properties. One of these sets concerns a case study in the context of text analysis. We provide a comprehensive empirical evaluation in two distinctive settings: (1) short-time execution with fixed beam size in order to evaluate the guidance abilities of the compared search heuristics; and (2) long-time executions with fixed target duration times in order to obtain high-quality solutions. In both settings, the newly proposed approach performs comparably to state-of-the-art techniques in the context of close-to-uniform instances and outperforms state-of-the-art approaches for non-uniform instances.

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Anytime pack search

Cited by 9 publications

References 32 publications

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Truncated incremental search

Solving the Longest Common Subsequence Problem Concerning Non-Uniform Distributions of Letters in Input Strings

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