In this paper, an enhanced unified differential evolution algorithm, named UDE-III, is presented for real parameterconstrained optimization problems. The proposed UDE-III is a significantly enhanced version of the Improved UDE (i.e., IUDE or UDE-II), which secured the 1st rank in the CEC 2018 competition on constrained real parameter optimization. UDE-II was inspired by the following state-of-the-art DE variants -CoDE, JADE, SaDE, DE with ranking-based mutation operator, SHADE, and C 2 oDE. To design UDE-III, we extensively analyzed and targeted the weaknesses of UDE-II. Specifically, UDE-III uses three trial vector generation strategies -DE/rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1. It is based on a dual population approach, and for each generation, it divides the current population into two sub-populations. The top subpopulation employs all three trial vector generation strategies on each target vector, just like in CoDE. On the other hand, in the bottom sub-population, one trial vector generation strategy is implemented on each target vector. The bottom sub-population employs strategy adaptation, wherein the probability of the three trial vector generation strategies is adapted in every generation by learning from their experiences in generating promising solutions in the previous learning period. The mutation operation in UDE-III is based on ranking-based mutation. Further, it employs the parameter adaptation principle of SHADE. The constraint handling principle in UDE-III is based on a combination of the feasibility rule and epsilon-constraint handling technique as proposed in C 2 oDE. We observed that stagnation is a major weakness of UDE-II. To overcome this weakness, we took inspiration from the best-discarded vector selection (BDVS) strategy proposed in the literature and integrated a novel strategy in UDE-III to address stagnation. Additionally, UDE-III considers the size of two sub-populations to be a design element, whereas in UDE-II, the two sub-populations were assumed to be of equal size. Moreover, in comparison to UDE-II, UDE-III improves upon the strategy adaptation, ranking-based mutation, and epsilon constraint handling component of the constraint handling technique. The proposed UDE-III algorithm is tested on the 28 benchmark 30D problems provided for the CEC 2024 competition on constrained real parameter optimization. UDE-III is compared with its predecessor, UDE-II, and the experimental results demonstrate the efficacy of the proposed algorithm in solving constrained real parameter optimization problems.
