An algorithm comparison for dynamic optimization problems

Authors:
Ignacio G. Del Amo;David A. Pelta;Juan R. GonzáLez;Antonio D. Masegosa
Affiliations:
Models of Decision and Optimization Research Group (MODO), Dept. of Computer Sciences and Artificial Intelligence, ICT Research Centre (CITIC-UGR), C/ Periodista Rafael Gomez, 2, University of Gra ...;Models of Decision and Optimization Research Group (MODO), Dept. of Computer Sciences and Artificial Intelligence, ICT Research Centre (CITIC-UGR), C/ Periodista Rafael Gomez, 2, University of Gra ...;Models of Decision and Optimization Research Group (MODO), Dept. of Computer Sciences and Artificial Intelligence, ICT Research Centre (CITIC-UGR), C/ Periodista Rafael Gomez, 2, University of Gra ...;Models of Decision and Optimization Research Group (MODO), Dept. of Computer Sciences and Artificial Intelligence, ICT Research Centre (CITIC-UGR), C/ Periodista Rafael Gomez, 2, University of Gra ...
Venue:
Applied Soft Computing
Year:
2012

Citing 16
Cited 1

Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence

Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Performance Measures for Dynamic Environments

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems

Advances in evolutionary computing
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)

Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
A self-organizing random immigrants genetic algorithm for dynamic optimization problems

Genetic Programming and Evolvable Machines
Reactive Search and Intelligent Optimization

Reactive Search and Intelligent Optimization
A study on diversity and cooperation in a multiagent strategy for dynamic optimization problems

International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
Learning behavior in abstract memory schemes for dynamic optimization problems

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on ICNC-FSKD’2008;Guest Editors: Liang Zhao, Maozu Guo, Lipo Wang
A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization

Journal of Heuristics
Cooperative Strategies and Reactive Search: A Hybrid Model Proposal

Learning and Intelligent Optimization
An analysis of particle properties on a multi-swarm PSO for dynamic optimization problems

CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments

IEEE Transactions on Evolutionary Computation
Optimization in dynamic environments: a survey on problems, methods and measures

Soft Computing - A Fusion of Foundations, Methodologies and Applications
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments

IEEE Transactions on Evolutionary Computation

The role of cardinality and neighborhood sampling strategy in agent-based cooperative strategies for Dynamic Optimization Problems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used.