Parallel Computing for Time-Consuming Multicriterial Optimization Problems

2017 ◽  
pp. 446-458 ◽  
Author(s):  
Victor Gergel ◽  
Evgeny Kozinov
Keyword(s):  
Parallel Computing ◽  
Optimization Problems ◽  
Multicriterial Optimization

A Study of Parallel Efficiency of Modified Direct Algorithm Applied to Thermohydrodynamic Lubrication

2009 ◽  
Vol 25 (2) ◽  
pp. 143-150 ◽  
Author(s):  
N. Wang ◽  
C.-M. Tsai ◽  
K.-C. Cha
Keyword(s):  
Parallel Computing ◽  
Optimization Problems ◽  
Load Capacity ◽  
Optimization Methods ◽  
Optimization Method ◽  
Parallel Optimization ◽  
Direct Algorithm ◽  
Slider Bearing ◽  
Parallel Efficiency ◽  
Fine Grain

AbstractThis study examines the parallel computing as a means to minimize the execution time in the optimization applied to thermohydrodynamic (THD) lubrication. The objective of the optimization is to maximize the load capacity of a slider bearing with two design variables. A global optimization method, DIviding RECTangle (DIRECT) algorithm, is used. The first approach was to apply the parallel computing within the THD model in a shared-memory processing (SMP) environment to examine the parallel efficiency of fine-grain computation. Next, a distributed parallel computing in the search level was conducted by use of the standard DIRECT algorithm. Then, the algorithm is modified to provide a version suitable for effective parallel computing. In the latter coarse-grain computation the speedups obtained by the DIRECT algorithms are compared with some previous studies using other parallel optimization methods. In the fine-grain computation of the SMP machine, the communication and overhead time costs prohibit high speedup in the cases of four or more simultaneous threads. It is found that the standard DIRECT algorithm is an efficient sequential but less parallel-computing-friendly method. When the modified algorithm is used in the slider bearing optimization, a parallel efficiency of 96.3% is obtained in the 16-computing-node cluster. This study presents the modified DIRECT algorithm, an efficient parallel search method, for general engineering optimization problems.

PARDEC: A Parallel Decomposition Algorithm for Non-Hierarchical Systems or Structures

1994 ◽  
Author(s):  
Hans A. Eschenauer ◽  
Matthias Weinert
Keyword(s):  
Parallel Computing ◽  
Shape Optimization ◽  
Optimization Problems ◽  
Computing System ◽  
Decomposition Algorithm ◽  
Hierarchical Systems ◽  
Parallel Decomposition

Abstract The present paper introduces a decomposition algorithm for non-hierarchical systems or structures. The algorithm coordinates the created subsystem optimization problems by means of an approximation strategy. It is implemented on a parallel computing system and will be verified on shape optimization problems.

A Two Level Parallel Evolution Strategy for Solving Mixed-Discrete Structural Optimization Problems

1995 ◽  
Author(s):  
Georg Thierauf ◽  
Jianbo Cai
Keyword(s):  
Parallel Computing ◽  
Structural Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Parallel Evolution ◽  
Evolution Strategy ◽  
Parallel Computer ◽  
Computing Architecture ◽  
Design Variables

Abstract A method for the solution of mixed-discrete structural optimization problems based on a two level parallel evolution strategy is presented. On the first level, the optimization problem is divided into two subproblems with discrete and continuous design variables, respectively. The two subproblems are solved simultaneously on a parallel computing architecture. On the second level, each subproblem is further parallelized by means of a parallel sub-evolution-strategy. Periodically, the design variables in the two groups axe exchanged. Examples are included to demonstrate the implementation of this method on a 8 nodes parallel computer.

scholarly journals Visekriterijumska optimizacija izbora izvodjaca projekta

2003 ◽  
Vol 44 (157) ◽  
pp. 7-40
Author(s):  
Jovo Vuleta
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Real Problem ◽  
Multicriterial Optimization ◽  
Past Experiences ◽  
The Given ◽  
Mathematical Operations ◽  
Theory Of Graphs ◽  
Project Realization ◽  
Selection Of

The selection of the best (multicriterial optimal) contractors for project realization is analised in this paper. This problem is one of the most important problems that occurs during the realization of every project, especially the complex one. First we point the problem importance and past experiences and results in its solving. As a conclusion, we state that the problem of selection project realization contractors has been solved by discovering any possible solution, not necessary the optimal one. We have tried to solve one real problem using the model of integer multicriterial optimization type 0-1. The problem was presented by the appropriate mathematical model whose solving leads to multicriterial optimal solution. The special attention was paid to technique and procedure for solving the given model of integer multicriterial optimization. In order to minimize the efforts, the model has been transformed in corresponding network model whose further solving is based on the theory of graphs. The presented procedure decreases the number of mathematical operations and is more simply than most of the usual methods for solving the integer multicriterial type 0-1 optimization problems. At the end, the recommended procedure has been illustrated by a numerical example.

scholarly journals Ddo, a Generic and Efficient Framework for MDD-Based Optimization

2020 ◽  
Author(s):  
Xavier Gillard ◽  
Pierre Schaus ◽  
Vianney Coppé
Keyword(s):  
Parallel Computing ◽  
Branch And Bound ◽  
Optimization Problems ◽  
Additional Benefit ◽  
Decision Diagrams ◽  
Constraint Optimization ◽  
Combinatorial Optimization Problems ◽  
Dynamic Programs ◽  
Generic Library ◽  
Constraint Optimization Problems

This paper presents ddo, a generic and efficient library to solve constraint optimization problems with decision diagrams. To that end, our framework implements the branch-and-bound approach which has recently been introduced by Bergman et al., (2016) to solve dynamic programs to optimality. Our library allowed us to successfully reproduce the results of Bergman et al. for MISP, MCP and MAX2SAT while using a single generic library. As an additional benefit, our ddo library is able to exploit parallel computing for its purpose without imposing any constraint on the user (apart from memory safety). Ddo is released as an open source rust library (crate) alongside with its companion example programs to solve the aforementioned problems. To the best of our knowledge, this is the first public implementation of a generic library to solve combinatorial optimization problems with branch-and-bound MDD.

Parallelization of Evolution Strategy on a Workstation-Cluster and its Application to Discrete Structural Optimization

1994 ◽  
Author(s):  
Jianbo Cai ◽  
Georg Thierauf
Keyword(s):  
Parallel Computing ◽  
Structural Optimization ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Biological Evolution ◽  
Computing Environment ◽  
Design Point ◽  
Workstation Cluster ◽  
Conventional Optimization ◽  
Discrete Optimization Problems

Abstract Evolution strategies (ESs) imitate biological evolution and have two characteristics that differ from other conventional optimization algorithms: (a) ESs use randomized operators instead of the usual deterministic ones; (b) instead of a single design point, the ESs work simultaneously with a population of design points in the space of variables. The second characteristic allows for an implementation in a parallel computing environment. In this paper the application of ESs for the solution of discrete optimization problems and its parallelization are described.

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