Generalized Adaptive Differential Evolution algorithm for Solving CEC 2020 Benchmark Problems

Abstract

The effort devoted in introducing new numerical optimization benchmarks has attracted the attention to develop new optimization algorithms to solve them. Very recently, a new suite on bound constrained optimization problems is proposed as a new addition to CEC benchmark series. Differential Evolution (DE) is a simple Evolutionary Algorithm (EA) which shows superior performance to solve many CEC benchmark during the past years. This paper presents a new extension to DE algorithm through extending the line of research for AGDE algorithm. The new algorithm, which we name GADE, enhanced the DE algorithm by introducing a generalized adaptive framework for enhancing the performance of DE. Numerical experiments on a set of 10 test problems from the CEC2020 benchmarks for 5, 10, 15 and 20 dimensions, including a comparison with state-of-the-art algorithm are executed. Comparative analysis indicates that GADE is superior to other state-of-the-art algorithms in terms of stability, robustness, and quality of solution.