MEMPSODE: an empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed

Authors:
Costas Voglis;Grigoris S. Piperagkas;Konstantinos E. Parsopoulos;Dimitris G. Papageorgiou;Isaac E. Lagaris
Affiliations:
University of Ioannina, Ioannina, Greece;University of Ioannina, Ioannina, Greece;University of Ioannina, Ioannina, Greece;University of Ioannina, Ioannina, Greece;University of Ioannina, Ioannina, Greece
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

Citing 6
Cited 2

Swarm intelligence

Swarm intelligence
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Memetic algorithms for continuous optimisation based on local search chains

Evolutionary Computation
Particle Swarm Optimization and Intelligence: Advances and Applications

Particle Swarm Optimization and Intelligence: Advances and Applications
Hybrid particle swarm optimization: an examination of the influence of iterative improvement algorithms on performance

ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Parameter selection and adaptation in Unified Particle Swarm Optimization

Mathematical and Computer Modelling: An International Journal

Adapt-MEMPSODE: a memetic algorithm with adaptive selection of local searches

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A parallel hybrid optimization algorithm for fitting interatomic potentials

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Memetic algorithms are hybrid schemes that usually integrate metaheuristics with classical local search techniques, in order to attain more balanced intensification/diversification trade--off in the search procedure. MEMPSODE is a recently published software that implements such memetic schemes, based on the Particle Swarm Optimization and Differential Evolution algorithms, as well as on the Merlin optimization environment that offers a variety of local search methods. The present study aims at investigating the impact of the selected local search algorithm in the memetic schemes produced by MEMPSODE. Our interest was focused on gradient--free local search methods. We applied the derived memetic schemes on the noiseless testbed of the Black--Box Optimization Benchmarking 2012 workshop. The obtained results can offer significant insight to optimization practitioners with respect to the most promising approaches.