  This study presents a population-based evolutionary optimization algorithm
(Adaptive Differential Evolution with Diversification Strategies or ADEDS). The
algorithm developed using the sinusoidal objective function and subsequently
evaluated with a wide-ranging set of 22 benchmark functions, including
Rosenbrock, Rastrigin, Ackley, and DeVilliersGlasser02, among others. The study
employs single-objective optimization in a two-dimensional space and runs ADEDS
on each of the benchmark functions with multiple iterations. In terms of
convergence speed and solution quality, ADEDS consistently outperforms standard
DE for a variety of optimization challenges, including functions with numerous
local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy
functions. This effectiveness holds great promise for optimizing supply chain
operations, driving cost reductions, and ultimately enhancing overall
performance. The findings imply the importance of effective optimization
strategy for improving supply chain efficiency, reducing costs, and enhancing
overall performance.
