A modified cultural algorithm with a balanced performance for the differential evolution frameworks

Numerous different methodologies have been introduced in the last few decades to provide efficient solutions for complex real-world problems and other optimization problems. This work focuses on the development of a simple hybrid cultural learning theme with a balanced performance for differential evolution frameworks. It is intended to be always efficient for a diverse set of optimization tasks. As different optimization algorithms behave differently depending on the problems, the combination of the best behaviors from different search strategies seems desirable. The proposed work explores the combination of the explorative/exploitative strengths of two heuristic search techniques, which discretely provide competitive results. Differential evolution is used as the population space for Cultural Algorithm, and is used to guide knowledge dissemination from the knowledge sources in the belief space. Here, a new influence function is introduced that adjusts the membership of each of the knowledge sources. The algorithm has been tested with the conditions and benchmark problems defined for the IEEE CEC2013 special session and competition on real-parameter single objective optimization. The paper also investigates the application of the new algorithm to a set of real-life problems concerning optimizing the weight a tension/compression spring and minimizing the fabrication cost of a welded beam engineering problem. The proposed algorithm appears to have a significant impact on the algorithmic functioning as it reliably augments the performance of the differential evolution frameworks with which it is integrated. Benchmark results for most of the synthetic functions from the special session show that the balanced hybrid obtains superior performance compared to the other competent algorithms. It scales well with the increasing dimensionality and converges in the close proximity of the global optimum for complex functions. (C) 2016 Elsevier B.V. All rights reserved.