  The existing variants of the Differential Evolution (DE) algorithm come with
certain limitations, such as poor local search and susceptibility to premature
convergence. This study introduces Adaptive Differential Evolution with
Diversification (ADED), a method that dynamically modifies the neighborhood
structure by evaluating the trial solutions' fitness. Developed to work with
both convex and nonconvex objective functions, ADED is validated with 22
benchmark functions, including Rosenbrock, Rastrigin, Ackley, and
DeVilliers-Glasser02. The development is carried out in Google Cloud using
Jupyter Notebook and Python v3.10.12, with additional testing conducted on the
multi-objective benchmark ZDT test suite. ADED distinguishes itself with its
adaptive and diverse approach, which includes adaptive mutation and
crossover-rates, diverse mutation tactics, diversification measurements, local
search mechanisms, and convergence monitoring. The unique combination of these
features collectively enhances ADED's effectiveness in navigating complex and
diverse landscapes, positioning it as a promising tool for addressing
challenges in both single- and multi-objective optimization scenarios.
