Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A Parallel Differential Evolution Algorithm A Parallel Differential Evolution Algorithm
PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Island Based Distributed Differential Evolution: An Experimental Study on Hybrid Testbeds
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
An enhanced memetic differential evolution in filter design for defect detection in paper production
Evolutionary Computation
Satellite Image Registration by Distributed Differential Evolution
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper proposes the introduction of a generator of random individuals within the ring topology of a Parallel Differential Evolution algorithm. The generated random individuals are then injected within a sub-population. A crucial point in the proposed study is that a proper balance between migration and random injection can determine the success of a distributed Differential Evolution scheme. An experimental study of this balance is carried out in this paper. Numerical results show that the proposed Parallel Random Injection Differential Evolution seems to be a simple, robust, and efficient algorithm which can be used for various applications. An important finding of this paper is that premature convergence problems due to an excessively frequent migration can be overcome by the injection of random individuals. In this way, the resulting algorithm maintains the high convergence speed properties of a parallel algorithm with high migration but differs in that it is able to continue improving upon the available genotypes and detect high quality solutions.