Journal of Global Optimization
Free search: a comparative analysis
Information Sciences—Informatics and Computer Science: An International Journal
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
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
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Differential evolution and non-separability: using selective pressure to focus search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Advances in Differential Evolution
Advances in Differential Evolution
Self-adaptive differential evolution
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration
Journal of Network and Computer Applications
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
An heterogeneous particle swarm optimizer with predator and scout particles
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Probabilistic stochastic diffusion search
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
International Journal of Applied Metaheuristic Computing
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Free Search Differential Evolution (FSDE) is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from Free Search (FS), Differential Evolution (DE) and opposition-based learning. The performance of the proposed approach is investigated and compared with DE and one of the recent variants of DE when applied to ten benchmark functions. The experiments conducted show that FSDE provides excellent results with the added advantage of no parameter tuning.