We examine the existing metrics of habitability and classification schemes of extrasolar planets and provide an exposition of the use of computational intelligence techniques to estimate habitability and to automate the process of classification of exoplanets. Exoplanetary habitability is a challenging problem in Astroinformatics, an emerging area in computational astronomy. The paper introduces a new constant elasticity habitability metric, the ‘Constant Elasticity Earth Similarity Approach (CEESA)’, to address the shortcoming of previous metrics. The proposed metric incorporates eccentricity as one of the component features to estimate the potential habitability of extrasolar planets. CEESA is a novel optimization model and computes habitability scores within the framework of a constrained optimization problem solved by metaheuristic method, mitigating the complexity and curvature violation issues in the process. The metaheuristic method, developed in the paper to solve the constrained optimization problem, is a ‘derivative-free’ optimization method, scope of which is promising beyond the current work. Habitabilty scores, such as CDHS (Bora et al., 2016), are recomputed with the imputed eccentricity values by the method developed in the paper and cross-matched with CEESA scores for validation. The paper also proposes fuzzy neural network-based approach to accomplish classification of exoplanets. Predicted class labels here are independent of CEESA, and are further validated by cross-matching them with the habitability scores computed by CEESA. We conclude by demonstrating the convergence between two proposed approaches, Earth-similarity approach (CEESA) and prediction of habitability labels (classification approach). The convergence between the two approaches establish the efficacy of CEESA in finding potentially habitable planets.

我们研究了现有的宜居性指标和太阳系外行星的分类方案，并提供了使用计算智能技术估算宜居性并使系外行星分类过程自动化的说明。在计算天文学的新兴领域-天文信息学中，系外行星的可居住性是一个具有挑战性的问题。本文介绍了一种新的恒定弹性可居住性度量标准，即“恒定弹性地球相似性方法（CEESA）”，以解决以前的度量标准的缺点。拟议的度量标准结合了偏心率作为估算太阳系外行星潜在可居住性的组成部分之一。CEESA是一种新颖的优化模型，可以在通过元启发式方法解决的约束优化问题的框架内计算可居住性得分，减轻了过程中的复杂性和曲率违规问题。本文为解决约束优化问题而开发的元启发式方法是一种“无导数”优化方法，其范围超出了当前的工作范围。通过本文开发的方法，用估算的偏心率值重新计算诸如CDHS（Bora等人，2016）的栖息地得分，并与CEESA得分进行交叉匹配以进行验证。本文还提出了基于模糊神经网络的方法来完成系外行星的分类。此处预测的类别标签与CEESA无关，并且通过将它们与CEESA计算的可居住性得分交叉匹配来进一步验证。最后，我们通过演示两种提议的方法之间的融合，地球相似性方法（CEESA）和可居住性标签的预测（分类方法）。两种方法的融合确立了CEESA在寻找潜在宜居行星方面的功效。