Extended Nondominated Sorting Genetic Algorithm (ENSGA-II) for Multi-Objective Optimization Problem in Interval Environment Asoke Kumar Bhunia, Amiya Biswas and Ali Akbar Shaikh 1 Introduction In the existing literature of optimization, most of the works have been reported for optimization of single objective. However, in real-world design or decision-making problems, it is often required to simultaneously optimize more than one objective functions which are conflicting in nature. The goal of these problems is to maximize or minimize several conflicting objectives simultaneously. These types of problems are known as multi-objective optimization problems. In this subarea, most of the works have been done in crisp environment. However, in reality, due to uncertainty or ambiguity, the parameters of the problems are not always precise and should be considered as imprecise. To represent the impreciseness of a parameter, several approaches like stochastic, fuzzy, fuzzy stochastic and interval approaches have been reported in the existing literature. Among these approaches, interval approach is more significant. Due to this representation, either all the objectives or some of the objectives would be interval valued. Thus, the general form of multi-objective optimization problem with interval objectives can be written as Minimize { A 1 (x ), A 2 (x ),..., A k (x )} A. K. Bhunia (B ) · A. A. Shaikh Department of Mathematics, The University of Burdwan, Burdwan 713104, West Bengal, India e-mail: akbhunia@math.buruniv.ac.in A. A. Shaikh e-mail: aliashaikh@math.buruniv.ac.in A. Biswas Department of Mathematics, A. B. N. Seal College, Cooch Behar 736101, India e-mail: amiya2al@gmail.com © Springer Nature Singapore Pte Ltd. 2018 J. K. Mandal et al. (eds.), Multi-Objective Optimization, https://doi.org/10.1007/978-981-13-1471-1_10 215