On the design of shared memory approaches to parallelize a multiobjective bee-inspired proposal for phylogenetic reconstruction

Santander-Jiménez, Sergio; Vega‐Rodríguez, Miguel A.

doi:10.1016/j.ins.2015.06.040

Cited by 9 publications

(6 citation statements)

References 42 publications

(91 reference statements)

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“…9 papers out of the 87 made use other algorithms for continuous optimization that were not PSO. The Artificial Bee Colony algorithm was found in 8 works ( [20][21][22][23][24][25]) while the Fish School Search algorithm was used in [26]. Tables 5 and 6 display the information about these papers.…”

Section: Other Algorithmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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Section: Other Algorithmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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“…The application of multi-objective optimization in phylogeny represents a hopeful solution to deal with main source of inconsistency that may affect the reliability of phylogenetic reasoning. According to [32], the study of phylogenetic reconstruction can be divided into two aspects. On the one hand, a series of multi-objective evolutionary algorithms have been successfully proposed to solve conflict information in different data sets.…”

Section: B Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction

Zhang

Zhong

et al. 2020

IEEE Access

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In recent years, inferring phylogenies has attracted lots of attention in both academic community and various application fields. Phylogenetic inference usually consists of a couple of evolutionary relationships, which can be represented as a phylogenetic tree. The phylogenetic reconstruction problem can be defined as an optimization problem, targeting at finding the most eligible tree among all possible topologies according to a selected criterion. Since the combinatorial number of possible topologies exceeds tolerance, various heuristic and metaheuristic methods have been proposed to find approximate solutions according to the selected criterion. However, different criterions are based on different principle and conflict with each other basically. In this line, scholars has proposed multi-objective evolutionary algorithm (MOEA) based on diverse criteria. Nevertheless, MOEA has suffered unbearable time consumption due to its inherent drawbacks of computational complexity and convergence. By studying the independence between the subpopulations in each time-consuming step of MOEA, the steps without global information can be designed to be executed in parallel, which can fundamentally address computational problems. Effective parallel algorithms designed with the characteristics of modern multicore clusters can solve such problems. In this sense, we propose a parallelized multi-objective evolutionary algorithm (MOEA-MC) by deploying on Spark, which added consensus into evolutionary algorithm to improve the quality of convergence and used membrane structure to keep equal solutions under different weights. In order to assess the performance achieved by the proposal, we have performed comparison among different methods on three real-world datasets separately. The results have certified that the solutions derived from MOEA-MC are superior to traditional methods in all studied datasets. And parallelized MOEA-MC can get dominant position and optimal Pareto-frontier simultaneously within minimal runtime.

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“…In shared memory machines, all processors have access to shared memory, and the communications between them is done by reading and writing on the shared memory. In distributed memory machines, each processor has its own local memory, and communication between processors is done by the interchange of messages through networks [9]. There are two general types of parallelization techniques, namely data parallelization and task parallelization.…”

Section: Introductionmentioning

confidence: 99%

A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

Pirozmand

Alrezaamiri

Ebrahimnejad

et al. 2021

MJCS

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Evolutionary algorithms are suitable methods for solving complex problems. Many changes have thus been made on their original structures in order to obtain more desirable solutions. Parallelization is a suitable technique to decrease the runtime of the algorithm, and therefore, to obtain solutions with higher quality. In this paper, a new algorithm is proposed with two approaches, which is based on a parallelization technique with shared memory architecture. In the proposed algorithm, the search space is firstly decomposed into multiple equal and independent subspaces. Then, a subtask is performed on each subspace simultaneously in a parallel manner which leads to providing more qualified solutions. Splitting the search space into smaller subspaces causes the algorithm to find optimal solutions in each region in an easier way. The algorithm RAPSO is improved with applying a new acceleration coefficient which has been named IRAPSO. In the proposed algorithm, the IRAPSO is used as the subtask. For the sake of testing the proposed algorithm, fourteen well-known benchmarks of numerical optimizing problems are inspected. Then, the proposed algorithm is compared with algorithms PPBO and PSOPSO that were both based on the island model. The results of the proposed algorithm are much better than those of the other two algorithms.

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On the design of shared memory approaches to parallelize a multiobjective bee-inspired proposal for phylogenetic reconstruction

Cited by 9 publications

References 42 publications

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallel MOEA Based on Consensus and Membrane Structure for Inferring Phylogenetic Reconstruction

A New Model of Parallel Particle Swarm Optimization Algorithm for Solving Numerical Problems

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