Effective competency based differential evolution algorithm

Abstract

The DE algorithm is considered as an efficient algorithm in the field of evolutionary algorithms. In case of DE the solution search process is a combination of evolutionary (evolution of population) and swarm intelligence (position update of individual) search process. In DE, the diversification and convergence of the population is controlled by the crossover rate (CR), the scale factor (F) and the vector differences of three randomly selected individuals. In DE algorithm, every solution is given equal chance to take part in the solution search and in case of stagnation; it is difficult to get out from this situation. Therefore, a competency based position update process is integrated in DE to boost the speed of convergence in addition to the diversification ability of the algorithm. The accuracy, efficiency, robustness, and reliability of the proposed algorithm, namely efficient competency based DE (ECDE) are analyzed over a set of 20 benchmark problems with diverse characteristics. Results are compared with DE and its two recent variants, namely FBDE, FSADE, and other algorithms, namely ABC and PSO.

Subject Classification:

• 46N10
• 05C85
• 68Rxx
• 68Wxx

Keywords:

• Competency based learning
• Evolutionary algorithms
• Nature inspired algorithms
• Swarm intelligence
• Optimization