This paper presents an application of elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. This scheduling problem is a bi-objective problem considering two objectives. The first objective is minimization of makespan and the second one being the minimization of flowtime. As a multi-objectiv...

Abstract. This paper examines the effect of crossover operations on the performance of EMO algorithms through computational experiments on knapsack problems and flowshop scheduling problems using the NSGA-II algorithm. We focus on the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. First we show the necessity of crossover operations t...

The Evolutionary Computation has grown much in last few years. Inspired by biological evolution, this field is used to solve NP-hard optimization problems to come up with best solution. TSP is most popular and complex problem used to evaluate different algorithms. In this paper, we have conducted a comparative analysis between NSGA-II, NSGA-III, SPEA-2, MOEA/D and VEGA to find out which algorit...

In this paper, we present a novel multi-objective mathematical model for capacitated single allocation hub location problem. There are the vehicle capacity constraint and capacity restrictions amount of the incoming flow to the hub while the balancing requirements of incoming quantities of flow to the each hub is considered. Moreover, there is a set of available capacities for each potential hu...

Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...

We present a multi-objective evolutionary algorithm to exploit a medium-sized fuzzy outranking relation to derive a partial order of classes of alternatives (we call it RP-NSGA-II). To measure the performance of RP-NSGA-II, we present an empirical study over a set of simulated multi-criteria ranking problems. The result of this study shows that RP-NSGA-II can effectively exploit a medium-sized ...