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.

通过异行星数据确定宜居性需要综合考虑多个参数，本文通过提出一种约束优化的多元、凸优化模型并使用改进的粒子群算法来确保全局最优，为寻找新的宜居星球提供指导。