A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
| Hi-index | 7.29 |
Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA). Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution.