Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Combined Swarm Differential Evolution Algorithm for Optimization Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
A population-based algorithm-generator for real-parameter optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Opposition-Based Learning: A New Scheme for Machine Intelligence
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Advances in Differential Evolution
Advances in Differential Evolution
Solving large scale optimization problems by opposition-based differential evolution (ODE)
WSEAS Transactions on Computers
Self-adaptive population sizing for a tune-free differential evolution
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Super-fit control adaptation in memetic differential evolution frameworks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
A hybrid particle swarm optimization approach with prior crossover differential evolution
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
A Modified Differential Evolution Algorithm and Its Application to Engineering Problems
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
An adaptive coevolutionary differential evolution algorithm for large-scale optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Free search differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dynamic optimization using self-adaptive differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A new differential evolution with wavelet theory based mutation operation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Mixed mutation strategy embedded differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Varying number of difference vectors in differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary adaptation of the differential evolution control parameters
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-start JADE with knowledge transfer for numerical optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution: difference vectors and movement in solution space
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution with laplace mutation operator
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Self-adaptive differential evolution
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Minimal representation multisensor fusion using differential evolution
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Operations Research Letters
Chaotic Evolution: fusion of chaotic ergodicity and evolutionary iteration for optimization
Natural Computing: an international journal
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
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Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/or premature convergence. In this article we propose a simple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role which helps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark problems, 7 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and three engineering design problems. Numerical results and statistical analysis show that the proposed MDE is better than or at least comparable to the basic DE and several other state-of-the art DE variants.