Non-dominated Sorting in Genetic Algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems. This paper investigates the application of NSGA-II technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller and a power system stabilizer. The design objective is to improve both rotor angle stability...

Today’s logistic systems in companies depend on optimum solutions of Facility Location-Allocation (FLA) problems in order to minimize cost values the company is dealing with. Therefore, FLA plays an important role in nowadays business environment. In this paper, a Hybrid Genetic Algorithm (HGA) is proposed to solve FLA. The HGA is a combination of Genetic Algorithm and Tabu Search while NSGA II...

When large sensor networks are applied to the task of target tracking, it is necessary to successively identify subsets of sensors that are most useful at each time instant. Such a task involves simultaneously maximizing target detection accuracy and minimizing querying cost, addressed in this paper by the application of multi-objective evolutionary algorithms (MOEAs). The objective of maximizi...

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application ra...

In this paper, a simple but efficient Non-dominated Sorting Genetic Algorithm (NSGA) II based technique is proposed for optimizing the Degree of Hybridization (DOH) in parallel passenger hybrid cars. The authors’ objective is to improve performance, maximize fuel economy and at the same time, minimize mass and emissions as much as possible, by optimal selection of DOH. The NSGA-II, which is a m...

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The cas...

Very recently, the first mathematical runtime analyses for NSGA-II, most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that NSGA-II optimizes OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, time such an advantage of proven ...