scholarly journals Automatic design of multi-objective local search algorithms

2017 ◽  
Author(s):  
Aymeric Blot ◽  
Laetitia Jourdan ◽  
Marie-Éléonore Kessaci
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Automatic Design ◽  
Local Search Algorithms ◽  
Multi Objective

Pareto local search algorithms for the multi-objective beam angle optimisation problem

2018 ◽  
Vol 24 (2) ◽  
pp. 205-238 ◽  
Author(s):  
Guillermo Cabrera-Guerrero ◽  
Andrew J. Mason ◽  
Andrea Raith ◽  
Matthias Ehrgott
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Beam Angle ◽  
Local Search Algorithms ◽  
Multi Objective ◽  
Pareto Local Search

scholarly journals Automatic Design of Hybrid Stochastic Local Search Algorithms

2013 ◽  
pp. 144-158 ◽  
Author(s):  
Marie-Eléonore Marmion ◽  
Franco Mascia ◽  
Manuel López-Ibáñez ◽  
Thomas Stützle
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Stochastic Local Search ◽  
Automatic Design ◽  
Local Search Algorithms

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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