Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing

2021 ◽  
Author(s):  
Syeed Abrar Zaoad ◽  
Tauhid Tanjim ◽  
Mir Hasan ◽  
Md. Mamun-Or-Rashid ◽  
Ibrahem Abdullah Almansour ◽  
...  
Keyword(s):  
Message Passing ◽  
Optimization Algorithms ◽  
Constraint Optimization

scholarly journals Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified directed communication

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lidija Fodor ◽  
Dušan Jakovetić ◽  
Nataša Krejić ◽  
Nataša Krklec Jerinkić ◽  
Srđan Škrbić
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Small Body ◽  
Optimization Algorithms ◽  
Empirical Evaluation ◽  
Big Data Analytics ◽  
Linear Convergence ◽  
Programming Environments ◽  
Trade Offs

AbstractThere has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.

scholarly journals Performance evaluation and analysis of distributed multi-agent optimization algorithms with sparsified communication

2020 ◽  
Author(s):  
Lidija Fodor ◽  
Dušan Jakovetić ◽  
Krejić Nataša ◽  
Nataša Krklec Jerinkić ◽  
Srđan Škrbić
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Small Body ◽  
Optimization Algorithms ◽  
Empirical Evaluation ◽  
Big Data Analytics ◽  
Linear Convergence ◽  
Programming Environments ◽  
Trade Offs

Abstract There has been significant interest in distributed optimization algorithms, motivated by applications in Big Data analytics, smart grid, vehicle networks, etc. While there have been extensive theory and theoretical advances, a proportionally small body of scientific literature focuses on numerical evaluation of the proposed methods in actual practical, parallel programming environments. This paper considers a general algorithmic framework of first and second order methods with sparsified communications and computations across worker nodes. The considered framework subsumes several existing methods. In addition, a novel method that utilizes unidirectional sparsified communications is introduced and theoretical convergence analysis is also provided. Namely, we prove R-linear convergence in the expected norm. A thorough empirical evaluation of the methods using Message Passing Interface (MPI) on a High Performance Computing (HPC) cluster is carried out and several useful insights and guidelines on the performance of algorithms and inherent communication-computational trade-offs in a realistic setting are derived.

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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