Local Search in Combinatorial Optimization

2018 ◽  
Keyword(s):  
Combinatorial Optimization ◽  
Local Search

scholarly journals Approximate Local Search in Combinatorial Optimization

2003 ◽  
Author(s):  
James B. Orlin ◽  
Abraham P. Punnen ◽  
Andreas S. Schulz
Keyword(s):  
Combinatorial Optimization ◽  
Local Search

scholarly journals Approximate Local Search in Combinatorial Optimization

2004 ◽  
Vol 33 (5) ◽  
pp. 1201-1214 ◽  
Author(s):  
James B. Orlin ◽  
Abraham P. Punnen ◽  
Andreas S. Schulz
Keyword(s):  
Combinatorial Optimization ◽  
Local Search

scholarly journals A Multi-Agent Proposal for the Resolution of BIBD instances

2016 ◽  
Vol 39 (2) ◽  
pp. 267 ◽  
Author(s):  
David Rodriguez ◽  
Enrique Darghan ◽  
Julio Monroy
Keyword(s):  
Combinatorial Optimization ◽  
Experimental Design ◽  
Tabu Search ◽  
Local Search ◽  
Current Literature ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Search Spaces ◽  
Multi Agent ◽  
Multi Agents

<p>The problem with designing balanced incomplete blocks (BIBD) is enclosed within the combinatorial optimization approach that has been extensively used in experimental design. The present proposal addresses thi problem by using local search techniques known as Hill Climbing, Tabu Search, and an approach based considerable sized the use of Multi-Agents, which allows the exploration of diverse areas of search spaces. Furthermore, the use of a vector vision for the consideration associated with vicinity is presented. The experimental results prove the advantage of this technique compared to other proposals that are reported in the current literature.</p>

scholarly journals Local Search with a SAT Oracle for Combinatorial Optimization

2021 ◽  
pp. 87-104
Author(s):  
Aviad Cohen ◽  
Alexander Nadel ◽  
Vadim Ryvchin
Keyword(s):  
Combinatorial Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Variable Neighbourhood Search ◽  
Np Hard ◽  
Neighbourhood Search ◽  
Sat Solver ◽  
Cell Placement

AbstractNP-hard combinatorial optimization problems are pivotal in science and business. There exists a variety of approaches for solving such problems, but for problems with complex constraints and objective functions, local search algorithms scale the best. Such algorithms usually assume that finding a non-optimal solution with no other requirements is easy. However, what if it is NP-hard? In such case, a SAT solver can be used for finding the initial solution, but how can one continue solving the optimization problem? We offer a generic methodology, called Local Search with SAT Oracle (), to solve such problems. facilitates implementation of advanced local search methods, such as variable neighbourhood search, hill climbing and iterated local search, while using a SAT solver as an oracle. We have successfully applied our approach to solve a critical industrial problem of cell placement and productized our solution at Intel.

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