Adaptive guided differential evolution algorithm with novel mutation for numerical optimization

• Published in: International journal of machine learning and cybernetics Vol. 10; no. 2; pp. 253 - 277
• Main Authors: Mohamed, Ali Wagdy, Mohamed, Ali Khater
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer Nature B.V
• Subjects: Artificial Intelligence; Complex Systems; Computational Intelligence; Control; Engineering; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Mechatronics; Mutation; Optimization; Original Article; Pattern Recognition; Robotics; Robustness (mathematics); Systems Biology; Differential evolution; Global optimization; Adaptive crossover; Evolutionary computation; Novel mutation
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-017-0711-7

This paper presents adaptive guided differential evolution algorithm (AGDE) for solving global numerical optimization problems over continuous space. In order to utilize the information of good and bad vectors in the DE population, the proposed algorithm introduces a new mutation rule. It uses two random chosen vectors of the top and the bottom 100 p % individuals in the current population of size NP while the third vector is selected randomly from the middle [ NP-2 (100 p %)] individuals. This new mutation scheme helps maintain effectively the balance between the global exploration and local exploitation abilities for searching process of the DE. Besides, a novel and effective adaptation scheme is used to update the values of the crossover rate to appropriate values without either extra parameters or prior knowledge of the characteristics of the optimization problem. In order to verify and analyze the performance of AGDE, Numerical experiments on a set of 28 test problems from the CEC2013 benchmark for 10, 30, and 50 dimensions, including a comparison with classical DE schemes and some recent evolutionary algorithms are executed. Experimental results indicate that in terms of robustness, stability and quality of the solution obtained, AGDE is significantly better than, or at least comparable to state-of-the-art approaches.