SPINEX-Optimization: Similarity-based Predictions with Explainable Neighbors Exploration for Single, Multiple, and Many Objectives Optimization

Abstract

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.