  This article introduces an expansion within SPINEX (Similarity-based
Predictions with Explainable Neighbors Exploration) suite, now extended to
single, multiple, and many objective optimization problems. The newly developed
SPINEX-Optimization algorithm incorporates a nuanced approach to optimization
in low and high dimensions by accounting for similarity across various
solutions. We conducted extensive benchmarking tests comparing
SPINEX-Optimization against ten single and eight multi/many optimization
algorithms over 55 mathematical benchmarking functions and realistic scenarios.
Then, we evaluated the performance of the proposed algorithm in terms of
scalability and computational efficiency across low and high dimensions, number
of objectives, and population sizes. The results indicate that
SPINEX-Optimization consistently outperforms most algorithms and excels in
managing complex scenarios, especially in high dimensions. The algorithm's
capabilities in explainability, Pareto efficiency, and moderate complexity are
highlighted through in-depth experiments and visualization methods.
