A variant of united multi-operator evolutionary algorithms with application to livestock feed ration optimization

The United Multi-Operator Evolutionary Algorithms (UMOEAs) demonstrate high performance in evolutionary computation due to their combination of various operators for Differential Evolution (DE) variants and local search mechanisms, and have evolved into three versions over the last decade. In this work, a novel variant of the UMOEAs is proposed, named UMOEAs-IV. UMOEAs-IV employs a novel calculation method for the scaling factor
, which generates dynamic values to adaptively control the step size of mutation operators, a mutation strategy with complementary operators that better balance exploration and exploitation, an Estimation-of-Distribution Algorithm (EDA) to learn the probabilistic distribution of promising individuals, and a stagnation strategy to help individuals escape local optima. UMOEAs-IV is compared with 15 recently proposed DE-based algorithms and tested on the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark functions, showing superior performance over all of them. It is also applied to livestock feed ration optimization for beef and dairy cattle, where it shows top performance in the experimental results.