scholarly journals NEIGHBORHOOD STRUCTURES FOR GPU-BASED LOCAL SEARCH ALGORITHMS

2010 ◽  
Vol 20 (04) ◽  
pp. 307-324 ◽  
Author(s):  
THÉ VAN LUONG ◽  
NOUREDINE MELAB ◽  
EL-GHAZALI TALBI
Keyword(s):  
Local Search ◽  
Gpu Computing ◽  
Search Space ◽  
Search Algorithms ◽  
Local Search Algorithms ◽  
Computation Process ◽  
Neighborhood Structures ◽  
Speed Up ◽  
Hard Problems

Local search algorithms are powerful heuristics for solving computationally hard problems in science and industry. In these methods, designing neighborhood operators to explore large promising regions of the search space may improve the quality of the obtained solutions at the expense of a high-cost computation process. As a consequence, the use of GPU computing provides an efficient way to speed up the search. However, designing applications on a GPU is still complex and many issues have to be faced. We provide a methodology to design and implement different neighborhood structures for LS algorithms on a GPU. The work has been evaluated for binary problems and the obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.

A New Neighborhood for the Job Shop Scheduling Problem

2012 ◽  
Vol 433-440 ◽  
pp. 1540-1544
Author(s):  
Mohammad Mahdi Nasiri ◽  
Farhad Kianfar
Keyword(s):  
Local Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithms ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Local Search Algorithms ◽  
Shop Scheduling ◽  
Neighborhood Structures

The effectiveness of the local search algorithms for shop scheduling problems is proved frequently. Local search algorithms like tabu search use neighborhood structures in order to obtain new solutions. This paper presents a new neighborhood for the job shop scheduling problem. In this neighborhood, few enhanced conditions are proposed to prevent cycle generation. These conditions allow that the neighborhood encompasses larger number of solutions without increasing the order of computational efforts.

scholarly journals Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem

2019 ◽  
Vol 27 (3) ◽  
pp. 525-558
Author(s):  
Mojgan Pourhassan ◽  
Frank Neumann
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Polynomial Time ◽  
Theoretical Perspective ◽  
Optimal Solution ◽  
Search Space ◽  
Search Algorithms ◽  
Variable Neighbourhood Search ◽  
Local Search Algorithms ◽  
Travelling Salesperson Problem

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl ( 2008 ) for solving this problem. In this article, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the node-based approach solves the hard instance of the cluster-based approach presented in Corus et al. ( 2016 ) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the node-based approach for a class of Euclidean instances.

The Local Search Algorithms for Maximum 3-Satisfiablility Problem

2010 ◽  
Vol 33 (7) ◽  
pp. 1127-1139
Author(s):  
Da-Ming ZHU ◽  
Shao-Han MA ◽  
Ping-Ping ZHANG
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Local Search Algorithms

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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