This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valvepoint loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED prob...

A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling n independent jobs onm identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this pap...

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-...

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated...

Most contemporary multi-objective evolutionary algorithms (MOEAs) store and handle a population with a linear list, and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes. This paper presents a data structure for storing the whole population and their dominating information in MOEAs. This structure, called a Dominance Tree (DT), ...

NSGA methodology discussed in Section 3.1 suffers from three weaknesses: computational complexity, non-elitist approach and the need to specify a sharing parameter. An improved version of NSGA known as NSGA-II, which resolved the above problems and uses elitism to create a diverse Pareto-optimal front, has been subsequently presented (Deb et al 2002). The main features of NSGA-II are low comput...