Hybridizing PSO and DE for improved vector evaluated multi-objective optimization

Authors:
Jacomine Grobler;Andries P. Engelbrecht
Affiliations:
Department of Industrial and Systems Engineering;Department of Computer Science, University of Pretoria, South Africa
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 8
Cited 0

Swarm intelligence

Swarm intelligence
Particle swarm optimization method in multiobjective problems

Proceedings of the 2002 ACM symposium on Applied computing
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

Evolutionary Computation
Fundamentals of Computational Swarm Intelligence

Fundamentals of Computational Swarm Intelligence
Multicriteria Scheduling: Theory, Models and Algorithms

Multicriteria Scheduling: Theory, Models and Algorithms
Multiobjective optimization using a Pareto differential evolution approach

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Performance assessment of multiobjective optimizers: an analysis and review

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a new vector evaluated multi-objective optimization algorithm. The vector evaluated differential evolution particle swarm optimization (VEDEPSO) algorithm is a hybridization of the classical vector evaluated particle swarm optimization (VEPSO) and vector evaluated differential evolution (VEDE) algorithms of Parsopoulos et. al. [9], [10]. Comparisons of VEDEPSO with respect to VEPSO and VEDE on a well known multi-objective benchmark problem set indicated that significant performance improvements can be attributed to the VEDEPSO algorithm.