A new memetic algorithm using particle swarm optimization and genetic algorithm

Authors:
Sang-Moon Soak;Sang-Wook Lee;N. P. Mahalik;Byung-Ha Ahn
Affiliations:
Dept. of Mechatronics, Gwangju Institute of Science and Technology, South Korea;Dept. of Mechatronics, Gwangju Institute of Science and Technology, South Korea;Dept. of Mechatronics, Gwangju Institute of Science and Technology, South Korea;Dept. of Mechatronics, Gwangju Institute of Science and Technology, South Korea
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 8
Cited 2

Crossover, Macromutationand, and Population-Based Search

Proceedings of the 6th International Conference on Genetic Algorithms
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Applying a Mutation-Based Genetic Algorithm to Processor Configuration Problems

ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Real-coded memetic algorithms with crossover hill-climbing

Evolutionary Computation - Special issue on magnetic algorithms
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

Evolutionary Computation
Particle swarm optimisation with spatial particle extension

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
On a property analysis of representations for spanning tree problems

EA'05 Proceedings of the 7th international conference on Artificial Evolution
The edge-window-decoder representation for tree-based problems

IEEE Transactions on Evolutionary Computation

Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report

Computers and Operations Research
Crossover-based local search in cooperative co-evolutionary feedforward neural networks

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a new memetic algorithm scheme combining a genetic algorithm (GA) and a particle swarm optimization algorithm (PSO). This memetic scheme uses the basic dynamics of PSO instead of the concept of the survival of fittest (selection strategy) in GA. Even though the scheme does not use a selection strategy, it shows that the algorithm can find good results and can be an alternative approach for network based optimization problems. We test it in the context of a memetic algorithm applied to well known spanning tree based optimization problem, the degree constrained minimum spanning tree problem (DCMST). We compare with existing evolutionary algorithms (EAs), including EA using edge window decoder and EA using edge-set encoding, which represent the current state of the art on the DCMST. The new memetic algorithm demonstrates superior performance on the smaller and lower degree instances of the well-used ‘Structured Hard' DCMST problems, and similar performance on the larger and higher degree instances.