Differential evolution optimization with time-frame strategy adaptation

In differential evolution (DE) research, many successful empirical guidelines in selecting appropriate trial vector generation strategies and control parameter values for various problems have been investigated. The comprehensive exploration of the experience can be an effective way to develop an advanced DE variant. In this paper, an improved DE approach with time-frame strategy adaptation called the time-frame adaptive differential evolution (TFADE) is proposed. It employs diverse trial vector generation strategies with various control parameter values that can be adaptively determined to generate promising solutions and dynamically adjusted to deal with premature convergence during evolution, according to successful experience over a period of preceding generations called the time frame. In the experimental study, TFADE is compared with 4 commonly used conventional DEs, 3 outstanding state-of-the-art adaptive DEs, and 2 novel non-DE approaches, evaluated by a test suite of 25 benchmark functions. The experimental results show that the performance of TFADE is significantly superior to these competitors.