Applications of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics

2006 ◽  
pp. 301-325 ◽  
Author(s):  
S. L. Martins ◽  
C. C. Ribeiro ◽  
I. Rosseti
Keyword(s):  
Optimization Problems ◽  
Parallel Metaheuristics

Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems

2012 ◽  
Vol 63 (3) ◽  
pp. 836-853 ◽  
Author(s):  
Mostepha R. Khouadjia ◽  
El-Ghazali Talbi ◽  
Laetitia Jourdan ◽  
Briseida Sarasola ◽  
Enrique Alba
Keyword(s):  
Dynamic Optimization ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Parallel Metaheuristics

A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS

2012 ◽  
Vol 23 (02) ◽  
pp. 445-464 ◽  
Author(s):  
YOUNG CHOON LEE ◽  
JAVID TAHERI ◽  
ALBERT Y. ZOMAYA
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Local Optima ◽  
Computer Clusters ◽  
Parallel Metaheuristics ◽  
Actual Solution ◽  
Parallel Hybrid ◽  
The Coordinator

A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.

scholarly journals Implementing cloud-based parallel metaheuristics: an overview

2018 ◽  
Vol 18 (03) ◽  
pp. e26
Author(s):  
Patricia González ◽  
Xoán Carlos Pardo Martínez ◽  
Ramón Doallo ◽  
Julio Banga
Keyword(s):  
Cloud Computing ◽  
Global Optimization ◽  
Optimization Problems ◽  
Programming Models ◽  
Interested Reader ◽  
Science And Engineering ◽  
Parallel Metaheuristics ◽  
Available Resources

Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel im- plementation applying HPC techniques is a common approach for efficiently using available resources to re- duce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuris- tics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.

scholarly journals Towards cloud-based parallel metaheuristics

2016 ◽  
Vol 32 (5) ◽  
pp. 693-705 ◽  
Author(s):  
Diego Teijeiro ◽  
Xoán C Pardo ◽  
Patricia González ◽  
Julio R Banga ◽  
Ramón Doallo
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Programming Models ◽  
Estimation Model ◽  
Acceptable Result ◽  
Parallel Metaheuristics

Many key problems in science and engineering can be formulated and solved using global optimization techniques. In the particular case of computational biology, the development of dynamic (kinetic) models is one of the current key issues. In this context, the problem of parameter estimation (model calibration) remains as a very challenging task. The complexity of the underlying models requires the use of efficient solvers to achieve adequate results in reasonable computation times. Metaheuristics have been the focus of great consideration as an efficient way of solving hard global optimization problems. Even so, in most realistic applications, metaheuristics require a very large computation time to obtain an acceptable result. Therefore, several parallel schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastructures. However, with the emergence of cloud computing, new programming models have been proposed to deal with large-scale data processing on clouds. In this paper we explore the applicability of these new models for global optimization problems using as a case study a set of challenging parameter estimation problems in systems biology. We have developed, using Spark, an island-based parallel version of Differential Evolution. Differential Evolution is a simple population-based metaheuristic that, at the same time, is very popular for being very efficient in real function global optimization. Several experiments were conducted both on a cluster and on the Microsoft Azure public cloud to evaluate the speedup and efficiency of the proposal, concluding that the Spark implementation achieves not only competitive speedup against the serial implementation, but also good scalability when the number of nodes grows. The results can be useful for those interested in using parallel metaheuristics for global optimization problems benefiting from the potential of new cloud programming models.

A Survey on the Metaheuristics Applied to QAP for the Graphics Processing Units

2016 ◽  
Vol 26 (03) ◽  
pp. 1650013 ◽  
Author(s):  
Omar Abdelkafi ◽  
Lhassane Idoumghar ◽  
Julien Lepagnot
Keyword(s):  
Graphics Processing Units ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Computational Cost ◽  
Combinatorial Problems ◽  
Quadratic Assignment ◽  
Processing Unit ◽  
Computational Power ◽  
Central Processing ◽  
Parallel Metaheuristics

The computational power requirements of real-world optimization problems begin to exceed the general performance of the Central Processing Unit (CPU). The modeling of such problems is in constant evolution and requires more computational power. Solving them is expensive in computation time and even metaheuristics, well known for their eficiency, begin to be unsuitable for the increasing amount of data. Recently, thanks to the advent of languages such as CUDA, the development of parallel metaheuristics on Graphic Processing Unit (GPU) platform to solve combinatorial problems such as the Quadratic Assignment Problem (QAP) has received a growing interest. It is one of the most studied NP-hard problems and it is known for its high computational cost. In this paper, we survey several of the most important metaheuristics approaches for the QAP and we focus our survey on parallel metaheuristics using the GPU.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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