Three-partition coevolutionary differential evolution algorithm for mixed-variable optimization problems

Both in industrial and scientific fields, many optimization problems involve continuous and discrete decision variables. Such problems are called mixed -variable optimization problems (MVOPs). MVOPs remain challenging due to the different spatial distribution characteristics of continuous, ordinal, and categorical variables. In this study, a new variant of differential evolution (DE), called the three -partition coevolutionary DE algorithm for MVOPs (TCDEmv) is proposed. First, a mixed -variable three -partition coevolutionary scheme that can simultaneously handle MVOPs comprising continuous, ordinal, and categorical variables with the same evolution operator is proposed. Additionally, the TCDEmv adopts a dynamic adaptive (DA) mechanism to maintain the balance between ordinal and categorical variables, avoiding the quantity dominance issue. Furthermore, to enhance the efficiency of the TCDEmv, a statistical probability -based two -layer optimization strategy (SPT) was employed for ordinal and categorical variables. The experimental results on 34 artificial MVOPs show that the TCDEmv obtained better solutions and convergence than seven representative algorithms. Compared with similar algorithms in three real -world MVOPs, the TCDEmv also shows competitive performance.