scholarly journals Stochastic Local Search Using the Search Space Smoothing Meta-Heuristic:A Case Study

2006 ◽  
Author(s):  
Sheqin Dong ◽  
Fan Guo ◽  
Jun Yuan ◽  
Rensheng Wang ◽  
Xianlong Hong
Keyword(s):  
Local Search ◽  
Search Space ◽  
Stochastic Local Search

Stochastic local search and parameters recommendation: a case study on flowshop problems

2020 ◽  
Author(s):  
Lucas M. Pavelski ◽  
Myriam Delgado ◽  
Marie‐Éléonore Kessaci ◽  
Alex A. Freitas
Keyword(s):  
Local Search ◽  
Stochastic Local Search

A stochastic local search approach to language tree reconstruction

Diachronica ◽  
2010 ◽  
Vol 27 (2) ◽  
pp. 341-358 ◽  
Author(s):  
Francesca Tria ◽  
Emanuele Caglioti ◽  
Vittorio Loreto ◽  
Andrea Pagnani
Keyword(s):  
Local Search ◽  
Phylogenetic Trees ◽  
Search Algorithm ◽  
Distance Matrix ◽  
Stochastic Local Search ◽  
Strongly Correlated ◽  
The Stability ◽  
Search Approach

In this paper we introduce a novel stochastic local search algorithm to reconstruct phylogenetic trees. We focus in particular on the reconstruction of language trees based on the comparison of the Swadesh lists of the recently compiled ASJP database. Starting from a generic tree configuration, our scheme stochastically explores the space of possible trees driven by the minimization of a pseudo-functional quantifying the violations of additivity of the distance matrix. As a consequence the resulting tree can be annotated with the values of the violations on each internal branch. The values of the deviations are strongly correlated with the stability of the internal edges; they are measured with a novel bootstrap procedure and displayed on the tree as an additional annotation. As a case study we considered the reconstruction of the Indo-European language tree. The results are quite encouraging, highlighting a potential new avenue to investigate the role of the deviations from additivity and check the reliability and consistency of the reconstructed trees.

scholarly journals An Improved Generic Bet-and-Run Strategy with Performance Prediction for Stochastic Local Search

2019 ◽  
Vol 33 ◽  
pp. 2395-2402
Author(s):  
Thomas Weise ◽  
Zijun Wu ◽  
Markus Wagner
Keyword(s):  
Local Search ◽  
Decision Maker ◽  
Performance Prediction ◽  
Generic Version ◽  
Time Budget ◽  
Search Space ◽  
Stochastic Local Search ◽  
A Posteriori ◽  
Recent Success ◽  
Baseline Approach

A commonly used strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. Building on the recent success of BET-AND-RUN approaches for restarted local search solvers, we introduce a more generic version that makes use of performance prediction. It is our goal to obtain the best possible results within a given time budget t using a given black-box optimization algorithm. If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We first start k ≥ 1 independent runs of the algorithm during an initialization budget t1 < t, pause these runs, then apply a decision maker D to choose 1 ≤ m < k runs from them (consuming t2 ≥ 0 time units in doing so), and then continue these runs for the remaining t3 = t−t1−t2 time units. In previous BET-AND-RUN strategies, the decision maker D = currentBest would simply select the run with the best-so-far results at negligible time. We propose using more advanced methods to discriminate between “good” and “bad” sample runs with the goal of increasing the correlation of the chosen run with the a-posteriori best one. In over 157 million experiments, we test different approaches to predict which run may yield the best results if granted the remaining budget. We show (1) that the currentBest method is indeed a very reliable and robust baseline approach, and (2) that our approach can yield better results than the previous methods.

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

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