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Continuous optimization is one of the most active research lines in evolutionary and metaheuristic algorithms. Since CEC 2005 and CEC 2008 competitions, many different algorithms have been proposed to solve continuous problems. Despite there exist very good algorithms reporting high quality results for a given dimension, the scalability of the search methods is still an open issue. Finding an algorithm...
In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems-A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100,...
The performance of population based search techniques like Differential Evolution (DE) depends largely on the selection of initial population. A good initialization scheme not only helps in giving a better final solution but also helps in improving the convergence rate of the algorithm. In the present study we propose a novel initialization scheme which uses the concept of quadratic interpolation...
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