A Novel Exoplanetary Habitability Score via Particle Swarm Optimization of CES Production Functions

TL;DR

This paper introduces a new method using modified Particle Swarm Optimization to accurately and efficiently compute habitability scores of exoplanets based on multiple parameters, aiding in the search for potentially habitable worlds.

Contribution

It presents a novel PSO-based approach with a dedicated Python library to optimize habitability scores considering complex constraints and avoiding local optima.

Findings

Effective mitigation of curvature violation and premature convergence.

Enhanced accuracy and efficiency in habitability scoring.

Provision of a Python library for broader application.

Abstract

The search for life has two goals essentially: looking for planets with Earth-like conditions (Earth similarity) and looking for the possibility of life in some form (habitability). Determining habitability from exoplanet data requires that determining parameters are collectively considered before coming up with a conclusion as no single factor alone contributes to it. Our proposed models, would serve as an indicator while looking for new habitable worlds, if computed with precision and efficiency. The models are of the type constrained optimization, multivariate, convex but may suffer from curvature violation and premature convergence impacting desired habitability scores. We mitigate the problem by proposing modified Particle Swarm Optimization (PSO) to tackle constraints and ensuring global optima. In the process, a python library to tackle such problems has been created.