Parallel Local Search

@inproceedings{Codognet2018ParallelLS,
  title={Parallel Local Search},
  author={Philippe Codognet and Danny M{\'u}nera and Daniel Diaz and Salvador Abreu},
  booktitle={Handbook of Parallel Constraint Reasoning},
  year={2018}
}
Local Search metaheuristics are a recognized means of solving hard combinatorial problems. Over the last couple of decades, significant advances have been made in terms of the formalization, applicability and performance of these methods. Key to the performance aspect is the increased availability of parallel hardware, which turns out to be largely exploitable by this class of procedures. As the real-life cases of combinatorial optimisation easily degrade into intractable territory for… 
Multithreaded incremental solving for local search based metaheuristics with step chasing
This work introduces a multithreaded solving methodology for local search based metaheuristics. It runs a single local search that spreads move evaluations across multiple threads. To preserve
Towards Distributed Local Search Through Neighborhood Combinators
This paper presents an approach for making local search algorithms distributed, to get speed improvements thanks to the growth both in multi-core hardware and the massive availability of distributed
Automatic Configuration of Multi-Thread Local Search: Preliminary Results on Bi-objective TSP
TLDR
This paper proposes a new highly parametric multi-thread and multiobjective local search algorithm dedicated to tackling optimisation problems and uses a configurator especially designed for multi-objective problems called MO-ParamILS in order to expose some best parallel configurations for the bi-Objective travelling salesman problem.
Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem
TLDR
This work proposes an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers, and presents experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems.
Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization
TLDR
This work addresses the Quadratic Assignment Problem using a local search technique, based on Extremal Optimization, and shows that cooperative parallel versions of the solver improve performance so much that large and hard instances can be solved quickly.
A State-of-the-art Review of Job-Shop Scheduling Techniques
TLDR
Assessment of the work done in the job-shop domain is sought by providing a review of many of the techniques used and it is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive.
Parallel Processing Algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future
TLDR
A review of the main approaches proposed over the past few years to solve combinatorial optimization problems in general and, in particular, the VRP and its different variants and shows an expansion of the use of parallel algorithms for solving various VRPs.
Estimating parallel runtimes for randomized algorithms in constraint solving
TLDR
A framework to estimate the parallel performance of a given algorithm by analyzing the runtime behavior of its sequential version by approximating the runtime distribution of the sequential process with statistical methods is proposed.
Handbook of Parallel Constraint Reasoning
TLDR
This chapter provides an overview of current approaches and their evolution over recent decades towards efficiently solving hard combinatorial problems on multi-core computers and clusters.
Widening: using parallel resources to improve model quality
TLDR
A unified description of Widening, a framework for the use of parallel (or otherwise abundant) computational resources to improve model quality, and some of the underlying constraints are softened so that Widening can be implemented in real world algorithms.
...
...

References

SHOWING 1-10 OF 130 REFERENCES
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms
TLDR
This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO, which has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.
COSEARCH: A Parallel Cooperative Metaheuristic
TLDR
An original design of the adaptive memory is proposed in order to focus on high quality regions of the search and avoid attractive but deceptive areas and the idiosyncrasies of the COSEARCH approach and its application for solving large scale instances of the quadratic assignment problem.
Iterated Local Search
TLDR
The purpose of this review is to give a detailed description of this metaheuristic and to show where it stands in terms of performance.
A survey on optimization metaheuristics
The Parallel Variable Neighborhood Search for the p-Median Problem
TLDR
The use of interchange moves provides a simple implementation of the VNS algorithm for the p-Median Problem and several strategies for the parallelization of theVNS are considered and coded in C using OpenMP.
GPU Computing for Parallel Local Search Metaheuristic Algorithms
TLDR
A new guideline for the design and implementation of effective LSMs on GPU is introduced and very efficient approaches are proposed for CPU-GPU data transfer optimization, thread control, mapping of neighboring solutions to GPU threads, and memory management.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
TLDR
A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
Massively Parallel Local Search for SAT
  • A. Arbelaez, P. Codognet
  • Computer Science
    2012 IEEE 24th International Conference on Tools with Artificial Intelligence
  • 2012
TLDR
This work studies the performance of parallel local search for SAT with a large degree of parallelism, up to 256 cores, and compares various cooperation strategies.
Parallel Local Search: Experiments with a PGAS-based programming model
TLDR
This paper discusses some experiments the team has been doing with Adaptive Search and presents a new parallel version of it based on GPI, a recent API and programming model for the development of scalable parallel applications.
...
...