NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

To meet the increasing computational demands, geographically distributed resources need to be logically coupled to make them work as a unified resource. In analyzing the performance of such distributed heterogeneous computing systems scheduling a set of tasks to the available set of resources for execution is highly important. Task scheduling being an NP-complete problem, use of metaheuristics ...

This paper visually demonstrates the effect of crossover operations on the performance of EMO algorithms through computational experiments on multi-objective 0/1 knapsack problems. In our computational experiments, we use the NSGA-II algorithm as a representative EMO algorithm. First we compare the performance of the NSGA-II algorithm between two cases: NSGA-II with/without crossover. Experimen...

In this paper, we have studied a multiobjective hybrid flowshop scheduling problem where n independent jobs should be executed in a hybrid assembly line. The aim of our work is to optimize the makespan and the total tardiness of the whole production. A simulation based optimization algorithm is proposed here to solve this problem. It is a combination of the simulation software ARENA and the FLC...

The present work focuses on evolving the multiple light-in-weight topologies of compliant mechanism tracing user defined path. Therefore in this paper, the bi-objective set is formulated first on the optimization frame-work in which the helper objective of maximum diversity is introduced with the primary objective of minimum weight of elastic structures. Thereafter, the evolutionary algorithm (...

This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two objective functions such as total payment function (TPF) for reactive power support from generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized simultaneous...

A multi-objective optimization problem (MOP) is often found in real-world optimization problem. Among various multiobjective optimization techniques, multi-objective evolutionary algorithm (MOEA) is highlighted as a good candidate due to its flexibility, feasibility, and its ability to handle multiple solutions. Among various MOEAs, we analyze 2MOEA which can achieve good convergence and divers...

In wireless sensor networks (WSNs), prototyping systems facilitate the realization of real node deployment, enabling to test new algorithms, proto‐ cols, and networking solutions. This paper investigates the 3D indoor redeploy‐ ment problem in WSNs by finding the positions where nodes should be added in order to improve an initial deployment while optimizing different objectives. For this purpo...

Abstract. Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algor...