Exploited Differential Evolution Algorithm
Aakanksha Bhatnagar, Kavita Sharma, Manoj Kumar Singh · 2015
Evolutionary algorithms are efficient algorithms for solving the most complex optimization problems of the current era. Differential Evolution (DE) is a simple population based evolutionary algorithm under this category. As shown in literature, c omparative to exploration of the search space, DE is less capable of exploiting the existing solutions. Therefore, DE is very much expected to skip the true optima. This paper proposes a variant of DE algorithm namely, exploited DE (EDE) which enhances the exploitation capability of DE search process by giving more chance to best solution of the current population to search further around itself. To validate the performance of proposed EDE algorithm, 10 mathematical benchmark optimization problems are considered. Experimental results on these problems of different complexities show that EDE is a promising DE variant and may be applied to solve complex optimization problems.