Parallel metaheuristics for combinatorial optimization (original) (raw)
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1995
In this paper, we review parallel search techniques for approximatingthe global optimal solution of combinatorial optimization problems. Recent developments on parallel implementation of genetic algorithms, simulated annealing, tabu search, and greedy randomized adaptive searchprocedures (GRASP) are discussed. Key words: Parallel Search, Heuristics, Genetic Algorithms, SimulatedAnnealing, Tabu Search, GRASP, Parallel Computing.
Chapter 1 PARALLEL STRATEGIES FOR METAHEURISTICS
2004
We present a state-of-the-art survey of parallel meta-heuristic develo pm nts and results, discuss general design and implementation principles that apply to most meta-heuristic classes, instantiate these principles for the three metaheuristic classes currently most extensively used genetic methods, simulate d annealing, and tabu search, and identify a number of trends and promising research directions.
1992
Metaheuristics -general search procedures whose principles allow them to escape the trap of local optimality using heuristic designs -have been successfully employed to address a variety of important optimization problems over the past few years. Particular gains have been achieved in obtaining high quality solutions to problems that classical exact methods (which guarantee convergence) have found too complex to handle effectively. Typically a metaheuristic method is crafted to suit the particular characteristics of the problem at hand, exploiting to the extent possible the structure available to enable a fruitful and efficient search process. An alternative to this problem specific solution approach is a more general methodology that recasts a given problem into a common modeling 1.
Parallel Metaheuristics Applications
A New Class of Algorithms, 2005
PARALLEL META-HEURISTICS APPLICATIONS problems, set covering and partitioning, satisfiability and max-sat problems, quadratic assignment, location and network design, traveling salesman and vehicle routing problems. We do not pretend to be exhaustive. We have also restricted to a minimum the presentation of general parallel computation issues as well as that of the parallel meta-heuristic strategies. The reader may consult a number of surveys, taxonomies, and syntheses of parallel meta-heuristics, of which quite a few address the "classical" meta-heuristics, Simulated Annealing, Genetic Algorithms, and Tabu Search, while some others address the field in more comprehensive terms:
Parallel strategies for meta-heuristics
2003
We present a state-of-the-art survey of parallel meta-heuristic developments and results, discuss general design and implementation principles that apply to most meta-heuristic classes, instantiate these principles for the three meta-heuristic classes currently most extensively used-genetic methods, simulated annealing, and tabu search, and identify a number of trends and promising research directions.
Improved Method for Parallelization of Evolutionary Metaheuristics
Mathematics, 2020
This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime...
On Integrating Population-Based Metaheuristics with Cooperative Parallelism
Many real-life applications can be formulated as Combinatorial Optimization Problems, the solution of which is often challenging due to their intrinsic difficulty. At present, the most effective methods to address the hardest problems entail the hybridization of metaheuristics and cooperative parallelism. Recently, a framework called CPLS has been proposed, which eases the cooperative parallelization of local search solvers. Being able to run different heuristics in parallel, CPLS has opened a new way to hybridize metaheuristics, thanks to its cooperative parallelism mechanism. However, CPLS is mainly designed for local search methods. In this paper we seek to overcome the current CPLS limitation, extending it to enable population-based metaheuristics in the hybridization process. We discuss an initial prototype implementation for Quadratic Assignment Problem combining a Genetic Algorithm with two local search procedures. Our experiments on hard instances of QAP show that this hybrid solver performs competitively w.r.t. dedicated QAP parallel solvers.
Parallel metaheuristics: recent advances and new trends
International Transactions in Operational Research, 2012
The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, ...
Designing Parallel Meta-Heuristic Methods
Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education, 2014
Meta-heuristic methods represent very powerful tools for dealing with hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in "reasonable" computing times. Moreover, a major issue in metaheuristic design and calibration is to make them robust, i.e., to provide high performance solutions for a variety of problem settings. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallel meta-heuristic ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, we explain various paradigms related to parallel meta-heuristic development, where communications, synchronization and control aspects are the most relevant. We also discuss implementation issues, namely the influence of the target architecture on parallel execution of meta-heuristics, pointing out the characteristics of shared and distributed memory multiprocessor systems. All these topics are illustrated by examples from recent literature. These examples are related to the parallelization of various meta-heuristic methods, but we focus here on Variable Neighborhood Search and Bee Colony Optimization.