`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Journal of Global Optimization
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Distributed Differential Evolution for the Registration of Remotely Sensed Images
PDP '07 Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing
Island Based Distributed Differential Evolution: An Experimental Study on Hybrid Testbeds
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Population size reduction for the differential evolution algorithm
Applied Intelligence
A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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
A probabilistic memetic framework
IEEE Transactions on Evolutionary Computation
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Parallel global optimisation meta-heuristics using an asynchronous island-model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Scale factor inheritance mechanism in distributed differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A clustering-based differential evolution for global optimization
Applied Soft Computing
A study on scale factor in distributed differential evolution
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A refactoring method for cache-efficient swarm intelligence algorithms
Information Sciences: an International Journal
Factorization of the coefficient variance matrix in orthogonaltransforms
IEEE Transactions on Signal Processing
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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As a population-based optimizer, the differential evolution (DE) algorithm has a very good reputation for its competence in global search and numerical robustness. In view of the fact that each member of the population is evaluated individually, DE can be easily parallelized in a distributed way. This paper proposes a novel distributed memetic differential evolution algorithm which integrates Lamarckian learning and Baldwinian learning. In the proposed algorithm, the whole population is divided into several subpopulations according to the von Neumann topology. In order to achieve a better tradeoff between exploration and exploitation, the differential evolution as an evolutionary frame is assisted by the Hooke-Jeeves algorithm which has powerful local search ability. We incorporate the Lamarckian learning and Baldwinian learning by analyzing their characteristics in the process of migration among subpopulations as well as in the hybridization of DE and Hooke-Jeeves local search. The proposed algorithm was run on a set of classic benchmark functions and compared with several state-of-the-art distributed DE schemes. Numerical results show that the proposed algorithm has excellent performance in terms of solution quality and convergence speed for all test problems given in this study.