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

TL;DR

SPINEX-Optimization is a novel algorithm that enhances similarity-based predictions for single, multiple, and many-objective optimization problems, demonstrating superior performance and explainability across diverse benchmarks.

Contribution

The paper extends SPINEX to handle various objective types, introducing a scalable, explainable optimization method with extensive benchmarking results.

Findings

Outperforms most compared algorithms in benchmarks

Effective in high-dimensional and multi-objective scenarios

Provides explainability and visualization of solutions

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.