IJR Project UMB-UHA-CESI

Since October 2017, I have been involving as a member of International Joint Research (IJR) project between 3 institutions (2 France, 1 Indonesian). Currently I’m an associate professor at UMB (maybe until [month] 20[xx] 🙂 ) and a PostDoc fellow at CESI Starsbourg, France, as a part of joint research between UMB-UHA-CESI. We have started our project on December 2017.

Non-dominated Sorting Genetic Algorithm

Non-dominated Sorting Genetic Algorithm, Nondominated Sorting Genetic Algorithm, Fast Elitist Non-dominated Sorting Genetic Algorithm, NSGA, NSGA-II

Taxonomy

The Non-dominated Sorting Genetic Algorithm is a Multiple Objective Optimization (MOO) algorithm and is an instance of an Evolutionary Algorithm from the field of Evolutionary Computation. Refer to for more information and references on Multiple Objective Optimization. NSGA is an extension of the Genetic Algorithm for multiple objective function optimization. It is related to other Evolutionary Multiple Objective Optimization Algorithms (EMOO) (or Multiple Objective Evolutionary Algorithms MOEA) such as the Vector-Evaluated Genetic Algorithm (VEGA), Strength Pareto Evolutionary Algorithm (SPEA), and Pareto Archived Evolution Strategy (PAES). There are two versions of the algorithm, the classical NSGA and the updated and currently canonical form NSGA-II.

Performance Analysis of Evolutionary Multi-Objective Based Approach for Deployment of Wireless Sensor Network with The Presence of Fixed Obstacles

Here is the abstract from our paper that already accepted and will be presented on Globecom 2014 that will be held in Austin, Texas, USA from 8 – 12 December 2014.

Abstract — In this paper, a study about wireless sensor network (WSN) deployment strategy is demonstrated and made workable for the use of multi-objective approach. The development of sensor nodes by considering multiple objectives and existence of fixed obstacles is an important optimization problem. There are two objectives in this study, connectivity and coverage as two fundamental issues in wireless sensor networks deployment. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address this problem. Two proposed functions, ranking function and fitness function, are used to determine the best optimal solution from Pareto optimal fronts. Further we presented simulation and analysis to verify and validate the deployment of wireless sensor network in area with the presence of permanent obstacles.

Keywords — Multi-objective optimization, Wireless Sensor Network, Deployment, Genetic Algorithm, Obstacle.