Iterative Differential Evolution with Real Parameter Encoding
Ashish Tripathi, Arun Kumar Singh, Amit Kumar Sirohi, Prem Chand Vashist · 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) · 2020
Evolutionary algorithms are a sub-discipline of artificial intelligence to solve various real-world problems. These algorithms are based on the Darwinian principle of evolution and so is the name evolutionary algorithm. Differential Evolution (DE) algorithm is a kind of evolutionary algorithms which are used for optimizing a problem mostly for real-valued functions. It uses random solutions and creates new solutions from the previous or existing solutions. This population based algorithm applies three operators namely selection, crossover and, mutation. In this work, a new strategy has been developed to improve the performance of the basic DE algorithm. Also, the resultant performance is compared to other optimization algorithms which show that modified DE is performing better than other existing algorithms.