A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systems
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Journal of Global Optimization
Hybrid simplex genetic algorithm for blind equalization using RBF networks
Mathematics and Computers in Simulation
A hybrid chaotic genetic algorithm for short-term hydro system scheduling
Mathematics and Computers in Simulation
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A multipopulation cultural algorithm using fuzzy clustering
Applied Soft Computing
Computing with the social fabric: The evolution of social intelligence within a cultural framework
IEEE Computational Intelligence Magazine
Evolutionary programming techniques for economic load dispatch
IEEE Transactions on Evolutionary Computation
Knowledge-based function optimization using fuzzy culturalalgorithms with evolutionary programming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling data envelopment analysis by chance method in hybrid uncertain environments
Mathematics and Computers in Simulation
Expert Systems with Applications: An International Journal
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Solving dynamic optimisation problems with revolutionary algorithms
International Journal of Innovative Computing and Applications
Minimum distance clustering algorithm based on an improved differential evolution
International Journal of Sensor Networks
Bio-inspired optimisation for economic load dispatch: a review
International Journal of Bio-Inspired Computation
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Evolutionary algorithms (EAs) are general-purpose stochastic search methods that use the metaphor of evolution as the key element in the design and implementation of computer-based problems solving systems. During the past two decades, EAs have attracted much attention and wide applications in a variety of fields, especially for optimization and design. EAs offer a number of advantages: robust and reliable performance, global search capability, little or no information requirement, and others. Among various EAs, differential evolution (DE), which characterized by the different mutation operator and competition strategy from the other EAs, has shown great promise in many numerical benchmark problems and real-world optimization applications. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. To improve the global optimization property of DE, in this paper, a DE approach based on measure of population's diversity and cultural algorithm technique using normative and situational knowledge sources is proposed as alternative method to solving the economic load dispatch problems of thermal generators. The traditional and cultural DE approaches are validated for two test systems consisting of 13 and 40 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Simulation results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving economic load dispatch problems.