Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

Abstract

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper. The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm's exploration and exploitation capability. A comprehensive test suite of standard benchmark problems with different natures, i.e., unimodal/multimodal and separable/non-separable, was used to test the convergence power of the proposed CTbADE algorithm. Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness, solution quality, and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms, such as jDE, CoDE, and EPSDE algorithms. This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computational models with a high number of parameters to adjust during the problem-solving process.

Improving Performance of Differential Evolution Using Multi-Population Ensemble Concept

“…The simplicity of the differential evolution algorithm uses mutation, crossover, and selection operations that make it competitive with other EAs to achieve effective results, and those operations also guide the EA population to achieve high performance. The DE algorithm has also been successfully applied to solve many other real-world problems in chemical engineering [12], economic dispatch problems [13], gene coevolution [14], circuit parameter optimizations [15], power systems [16], medical imaging [17], and function optimization [18].…”