Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Swarm intelligence
Journal of Global Optimization
Ant Colony Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Advances in Differential Evolution
Advances in Differential Evolution
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
A differential evolution algorithm with intersect mutation operator
Applied Soft Computing
Sequential approximate multi-objective optimization using radial basis function network
Structural and Multidisciplinary Optimization
International Journal of Swarm Intelligence Research
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In this paper, the basic characteristics of the differential evolution (DE) are examined. Thus, one is the meta-heuristics, and the other is the global optimization technique. It is said that DE is the global optimization technique, and also belongs to the meta-heuristics. Indeed, DE can find the global minimum through numerical experiments. However, there are no proofs and useful investigations with regard to such comments. In this paper, the DE is compared with the generalized random tunneling algorithm (GRTA) and the particle swarm optimization (PSO) that are the global optimization techniques for continuous design variables. Through the examinations, some common characteristics as the global optimization technique are clarified in this paper. Through benchmark test problems including structural optimization problems, the search ability of DE as the global optimization technique is examined.