Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to differential evolution
New ideas in optimization
Mechanical engineering design optimization by differential evolution
New ideas in optimization
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Journal of Global Optimization
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Inference of gene regulatory networks using s-system and differential evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Computing Nash equilibria through computational intelligence methods
Journal of Computational and Applied Mathematics - Special issue: Selected papers of the international conference on computational methods in sciences and engineering (ICCMSE-2003)
Inference of genetic networks using S-system: information criteria for model selection
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A novel population initialization method for accelerating evolutionary algorithms
Computers & Mathematics with Applications
Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Population size reduction for the differential evolution algorithm
Applied Intelligence
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
DEEP - Differential Evolution Entirely Parallel Method for Gene Regulatory Networks
PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
Distributed differential evolution with explorative---exploitative population families
Genetic Programming and Evolvable Machines
Enhancing differential evolution frameworks by scale factor local search: part I
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
Evolutionary adaptation of the differential evolution control parameters
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A statistical study of the differential evolution based on continuous generation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Computing Nash equilibria through computational intelligence methods
Journal of Computational and Applied Mathematics - Special issue: Selected papers of the international conference on computational methods in sciences and engineering (ICCMSE-2003)
Differential evolution algorithm based on simulated annealing
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A clustering-based differential evolution for global optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis
Expert Systems with Applications: An International Journal
A novel differential evolution using a mixed mutation strategy
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Evolving ant direction differential evolution for OPF with non-smooth cost functions
Engineering Applications of Artificial Intelligence
A study on scale factor in distributed differential evolution
Information Sciences: an International Journal
Disturbed Exploitation compact Differential Evolution for limited memory optimization problems
Information Sciences: an International Journal
Differential evolution with improved mutation strategy
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
DEEP--differential evolution entirely parallel method for gene regulatory networks
The Journal of Supercomputing
Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Enhancing the search ability of differential evolution through orthogonal crossover
Information Sciences: an International Journal
Self-adaptive randomized and rank-based differential evolution for multimodal problems
Journal of Global Optimization
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
Differential evolution with modified mutation strategy for solving global optimization problems
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
The modified differential evolution algorithm (MDEA)
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
An intuitive distance-based explanation of opposition-based sampling
Applied Soft Computing
Principal coordinate strategy: a novel adaptive control strategy for differential evolution
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Multi-agent simulated annealing algorithm based on differential evolution algorithm
International Journal of Bio-Inspired Computation
Information Sciences: an International Journal
Simultaneous estimation of thin film thickness and optical properties using two-stage optimization
Journal of Global Optimization
Adaptive population tuning scheme for differential evolution
Information Sciences: an International Journal
A study on scale factor/crossover interaction in distributed differential evolution
Artificial Intelligence Review
Inferring large scale genetic networks with S-system model
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
A new hybrid differential evolution with simulated annealing and self-adaptive immune operation
Computers & Mathematics with Applications
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Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. However, the convergence rate of differential evolution in optimizing a computationally expensive objective function still does not meet all our requirements, and attempting to speed up DE is considered necessary. In this paper, a new local search operation, trigonometric mutation, is proposed and embedded into the differential evolution algorithm. This modification enables the algorithm to get a better trade-off between the convergence rate and the robustness. Thus it can be possible to increase the convergence velocity of the differential evolution algorithm and thereby obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be advantageous in many real-world problems where the evaluation of a candidate solution is a computationally expensive operation and consequently finding the global optimum or a good sub-optimal solution with the original differential evolution algorithm is too time-consuming, or even impossible within the time available. In this article, the mechanism of the trigonometric mutation operation is presented and analyzed. The modified differential evolution algorithm is demonstrated in cases of two well-known test functions, and is further examined with two practical training problems of neural networks. The obtained numerical simulation results are providing empirical evidences on the efficiency and effectiveness of the proposed modified differential evolution algorithm.