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A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the...
The particle swarm optimizer (PSO) is a popular computing technique of swarm intelligence, known for its fast convergence speed and easy implementation. All the particles in the traditional PSO must learn from the best-so-far solution, which makes the best solution the leader of the swarm. This paper proposes a variation of the traditional PSO, named the PSO with lifespan (LS-PSO), in which the lifespan...
Differential evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. This article provides a simple mathematical model of the underlying evolutionary dynamics of a one-dimensional DE. The model relates the search process of DE with the classical gradient descent search and also analyzes the convergence behavior of a DE population, very near...
The Differential Evolution (DE) is a stochastic population-based search method for global optimization over continuous spaces. This paper presents an efficient strategy for self-adapting control parameters in Differential Evolution to solve real-parameter optimization problems. The proposed strategy introduces an adaptive mechanism at the individual level based on Cauchy distribution (CD) where the...
A global optimization algorithm combining an adaptive response surface approximation of the objective function and experiment design strategy is presented. In the adaptive approximation, an optimal Latin hypercube sampling strategy based on multi-objective Pareto optimization is developed to obtain the sampling data in the design variable space, and multiquadric radial basis function is employed to...
Evolutionary algorithms have been very successful at solving global optimization problems. Two competing goals govern the performance of evolutionary algorithms: exploration and exploitation. This paper proposes a new heuristic to keep population diversity: the shake and the regicide. The shake heuristic improves the exploration by perturbing the whole population. The regicide heuristic (kill the...
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