Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
AMA: a new approach for solving constrained real-valued optimization problems
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
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
A simple ranking and selection for constrained evolutionary optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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This paper presents a novel Constrained Optimization based on Modified Differential Evolution algorithm (COMDE). In the new algorithm, a new directed mutation rule, based on the weighted difference vector between the best and the worst individuals at a particular generation, is introduced. The new directed mutation rule is combined with the modified basic mutation strategy DE/rand/1/bin, where only one of the two mutation rules is applied with the probability of 0.5. The proposed mutation rule is shown to enhance the local search ability of the basic Differential Evolution (DE) and to get a better trade-off between convergence rate and robustness. Two new scaling factors are introduced as uniform random variables to improve the diversity of the population and to bias the search direction. Additionally, a dynamic non-linear increased crossover probability is utilized to balance the global exploration and local exploitation. COMDE also includes a modified constraint handling technique based on feasibility and the sum of constraints violations. A new dynamic tolerance technique to handle equality constraints is also adopted. The effectiveness and benefits of the new directed mutation strategy and modified basic strategy used in COMDE has been experimentally investigated. The effect of the parameters of the crossover probability function and the parameters of the dynamic tolerance equation on the performance of COMDE have been analyzed and evaluated by different experiments. Numerical experiments on 13 well-known benchmark test functions and five engineering design problems have shown that the new approach is efficient, effective and robust. The comparison results between the COMDE and the other 28 state-of-the-art evolutionary algorithms indicate that the proposed COMDE algorithm is competitive with, and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final solution.