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Related Experiment Video

Updated: May 23, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Asynchronous master-slave parallelization of differential evolution for multi-objective optimization.

Matjaž Depolli1, Roman Trobec, Bogdan Filipič

  • 1Department of Communication Systems, Jožef Stefan Institute, SI-1000, Ljubljana, Slovenia. matjaz.depolli@ijs.si

Evolutionary Computation
|March 29, 2012
PubMed
Summary
This summary is machine-generated.

AMS-DEMO, an asynchronous evolutionary algorithm, efficiently solves complex multi-objective optimization problems on parallel computers. It introduces a novel asynchronous master-slave approach, outperforming synchronous methods by mitigating selection lag.

Related Experiment Videos

Last Updated: May 23, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

  • Computational Science
  • Optimization Algorithms
  • Parallel Computing

Background:

  • Multi-objective optimization problems are computationally intensive.
  • Existing evolutionary algorithms often struggle with time-intensive problems.
  • Parallel computing offers a solution but requires efficient algorithm design.

Purpose of the Study:

  • To present AMS-DEMO, an asynchronous master-slave implementation of the DEMO evolutionary algorithm.
  • To investigate the asynchronous master-slave parallelization of multi-objective optimization.
  • To analyze the performance of AMS-DEMO on homogeneous and heterogeneous parallel architectures.

Main Methods:

  • Developed AMS-DEMO, an asynchronous master-slave parallelization of the DEMO algorithm.
  • Analyzed the 'selection lag' phenomenon in parallel optimization.
  • Tested AMS-DEMO on benchmark and industrial time-intensive problems.
  • Compared AMS-DEMO with a synchronous (generational) master-slave DEMO.

Main Results:

  • AMS-DEMO efficiently solves time-intensive multi-objective optimization problems.
  • Selection lag is identified as a key factor influencing performance based on architecture and processor count.
  • Empirical results validate analytical findings on selection lag.
  • AMS-DEMO demonstrates superior performance over synchronous parallelization.

Conclusions:

  • Asynchronous master-slave parallelization is effective for multi-objective optimization.
  • AMS-DEMO offers significant performance enhancements for time-intensive problems.
  • Understanding selection lag is crucial for optimizing parallel evolutionary algorithms.