Measuring the Performance of Parallel Metaheuristics

2005 ◽  
pp. 43-62 ◽  
Author(s):  
Enrique Alba ◽  
Gabriel Luque
Keyword(s):  
Parallel Metaheuristics

Shop floor simulation optimization using machine learning to improve parallel metaheuristics

2020 ◽  
Vol 150 ◽  
pp. 113272 ◽  
Author(s):  
Wilson Trigueiro de Sousa Junior ◽  
José Arnaldo Barra Montevechi ◽  
Rafael de Carvalho Miranda ◽  
Mona Liza Moura de Oliveira ◽  
Afonso Teberga Campos
Keyword(s):  
Machine Learning ◽  
Simulation Optimization ◽  
Shop Floor ◽  
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 A Parallel Multiobjective Metaheuristic for Multiple Sequence Alignment

2017 ◽  
Author(s):  
Álvaro Rubio-Largo ◽  
Leonardo Vanneschi ◽  
Mauro Castelli ◽  
Miguel A. Vega-Rodríguez
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Time Complexity ◽  
Optimization Problem ◽  
Optimal Alignment ◽  
Multiple Sequence ◽  
Parallel Metaheuristics ◽  
Parallel Performance ◽  
Parallel Version ◽  
The Comparative Study

AbstractThe alignment among three or more nucleotides/amino-acids sequences at the same time is known as Multiple Sequence Alignment (MSA), an NP-hard optimization problem. The time complexity of finding an optimal alignment raises exponentially when the number of sequences to align increases. In this work, we deal with a multiobjective version of the MSA problem where the goal is to simultaneously optimize the accuracy and conservation of the alignment. A parallel version of the Hybrid Multiobjective Memetic Metaheuristics for Multiple Sequence Alignment is proposed. In order to evaluate the parallel performance of our proposal, we have selected a pull of datasets with different number of sequences (up to 1000 sequences) and study its parallel performance against other well-known parallel metaheuristics published in the literature, such as MSAProbs, T-Coffee, Clustal Ω, and MAFFT. The comparative study reveals that our parallel aligner is around 25 times faster than the sequential version with 32 cores, obtaining a parallel efficiency around 80%.

Parallel Metaheuristics in Telecommunications

2005 ◽  
pp. 495-515
Author(s):  
Sergio Nesmachnow ◽  
Héctor Cancela ◽  
Enrique Alba ◽  
Francisco Chicano
Keyword(s):  
Parallel Metaheuristics
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