Parallel computation: models and methods
Parallel computation: models and methods
MPI: The Complete Reference
Journal of Global Optimization
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
A Parallel Genetic Algorithm for Multiobjective Microprocessor Design
Proceedings of the 6th International Conference on Genetic Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Intelligent Zoning Design Using Multi-Objective Evolutionary Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Hierarchical parallel approach for GSM mobile network design
Journal of Parallel and Distributed Computing
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Grid'5000: A Large Scale And Highly Reconfigurable Experimental Grid Testbed
International Journal of High Performance Computing Applications
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Differential evolution versus genetic algorithms in multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A study of the parallelization of the multi-objective metaheuristic MOEA/D
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Challenges of applying optimization methodology in industry
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed for solving time-intensive problems efficiently on both homogeneous and heterogeneous parallel computer architectures. The algorithm is used as a test case for the asynchronous master-slave parallelization of multi-objective optimization that has not yet been thoroughly investigated. Selection lag is identified as the key property of the parallelization method, which explains how its behavior depends on the type of computer architecture and the number of processors. It is arrived at analytically and from the empirical results. AMS-DEMO is tested on a benchmark problem and a time-intensive industrial optimization problem, on homogeneous and heterogeneous parallel setups, providing performance results for the algorithm and an insight into the parallelization method. A comparison is also performed between AMS-DEMO and generational master-slave DEMO to demonstrate how the asynchronous parallelization method enhances the algorithm and what benefits it brings compared to the synchronous method.