Flávio Vinícius Cruzeiro Martins

A Hybrid Multiobjective Evolutionary Approach for Improving the Performance of Wireless Sensor Networks

The increasing in the demand for Wireless Sensor Networks (WSNs) has intensified studies which are dedicated to obtain more energy-efficient solutions, since the energy storage limitation is critical in those systems. Additionally, there are other aspects which usually must be ensured in order to get an acceptable performance of WSNs, such as area coverage and network connectivity. This paper proposes a procedure for enhancing the performance of WSNs: a multiobjective hybrid optimization algorithm is employed for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSNs subjected to node failures. This method combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local on line algorithm (LoA), which is intended to restore the network coverage soon after any failure. The proposed approach is compared with an Integer Linear Programming (ILP)-based approach and a similar mono-objective approach with regard to coverage, network lifetime and required running time for achieving the optimal solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can improve the performance of the WSN obtaining good solutions with a considerably smaller computational time than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.

A genetic algorithm for deploying roadside units in VANETs

In this work we propose a genetic algorithm, Delta-GA, for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our goal is to find the minimum set of RSUs in order to meet a Deployment Δ^ρ1_ρ2. The Deployment Δ^ρ1_ρ2 is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare Delta-GA to two baseline algorithms, Delta-g and Delta-r, to solve the Deployment Δ^ρ1_ρ2. Our results demonstrate that Delta-GA requires less Roadside Units in order to achieve the same deployment efficiency.

On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks.

Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the systems dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

An Evolutionary Dynamic Approach for Designing Wireless Sensor Networks for Real Time Monitoring

The evolution in the microelectronics and embedded systems has expanded the employment of Wireless Sensor Networks (WSNs). The energy limitation of the nodes is a very important restriction of those structures and should be always considered during the network design. The search for energy-efficient WSNs must take into account aspects which are essential for the proper operation of the network, such as area coverage and network connectivity. This paper proposes an evolutionary approach for performing the design of WSNs, considering the dynamic nature of the problem. A genetic algorithm, which aims to maximize the lifetime of the network, is employed for establishing the sequence in which the sensor nodes are activated, ensuring that the minimum coverage (established a priori) and the connectivity constraints are met. Results achieved by the proposed algorithm in a 81-sensor node instance are compared with a former work, in order to validate the approach which is presented here.

Optimization of the vehicle routing problem with demand responsive transport using the NSGA-II algorithm

Demand Responsive Transport (DRT) systems emerge as an alternative to deal with the problem of variable demand, or even unpredictable, occurring in conventional urban transport systems. It can be seen in some practical situations such as public transport in rural areas, wherein in some situations, there is no way to predict demand. This paper addresses the Vehicle Routing Problem with Demand Responsive Transport (VRPDRT), a type of transport which enables customers to be taken to their destination like a taxi or minibus in order to reduce operating costs and to meet customer needs. A multiobjective approach is proposed to VRPDRT in which five different objective functions are used. These functions are aggregated in three new functions resulting in a three-objective formulation for VRPDRT. When using a three objective approach, that formulation allows a better understanding of the company and human perspectives while permitting to solve the resulting problem in an efficient way. The proposed three-objective optimization problem is solved using a random method of generating solutions and an algorithm considered state of the art, the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The sets of solutions are compared using the Set Coverage Metric. The results show that the NSGA-II algorithm could obtain sets of solutions with better values for all objective functions used also called the non-dominated solutions set.

Multiobjective approach to the vehicle routing problem with demand responsive transport

The Vehicle Routing Problem (VRP) has been largely studied over the last years, since problems involving the transport of persons and/or goods have great practical application. This paper addresses the Vehicles Routing Problem with Demand Responsive Transport (VRPDRT), a type of transport which enables customers to be taken to your destination like a taxi or minibus in order to reduce operating costs and to meet customer needs. A multiobjective approach is proposed to VRPDRT in which five different objective functions are used. Using an iterative methodology, known as aggregation tree, the objective functions are used to construct a bi-objective version for the problem. The proposed bi-objective optimization problem is solved via NSGA-II and SPEA2 and the algorithm performances are compared using S-Metric. Through a statistical test, the results shows with 95% of confidence that the NSGA-II presents better convergence when compared with SPEA2.

Dimensionality Reduction Approach for Many-Objective Vehicle Routing Problem with Demand Responsive Transport

Demand Responsive Transport DRT systems emanate as a substitute to face the problem of volatile, or even inconstant, demand, occurring in popular urban transport systems. This paper is focused in the Vehicle Routing Problem with Demand Responsive Transport VRPDRT, a type of transport which enables passengers to be taken to their destination, as a shared service, trying to minimize the company costs and offer a quality service taking passengers on their needs. A many-objective approach is applied in VRPDRT in which seven different objective functions are used. To solve the problem through traditional multi-objective algorithms, the work proposes the usage of cluster analysis to perform the dimensionaly reduction task. The seven functions are then aggregated resulting in a bi-objective formulation and the algorithms NSGA-II and SPEA 2 are used to solve the problem. The results show that the algorithms achieve statistically different results and NSGA-II reaches a greater number of non-dominated solutions when compared to SPEA 2. Furthermore, the results are compared to an approach proposed in literature that uses another way to reduce the dimensionality of the problem in a two-objective formulation and the cluster analysis procedure is proven to be a competitive methodology in that problem. It is possbile to say that the behavior of the algorithm is modified by the way the dimensionality reduction of the problem is made.

CardNutri: A Software of Weekly Menus Nutritional Elaboration for Scholar Feeding Applying Evolutionary Computation

This paper aims to present and evaluate a software that uses an evolutionary strategy to design weekly nutritional menus for School Feeding. The software ensures the nutritional needs of students and also minimizes the total cost of the menu. We based our nutritional needs on the Brazilian National School Feeding Programme (PNAE). This program takes into account: (i) the age of the student; (ii) some preparations issues as color, consistency and, variety; and also (iii) the maximum amount to be paid per meal. Our software generates, in less than five minutes, a set of menus, and the nutritionist can choose the menu that suits his/her best. We evaluate our algorithm using the Weighted-Sum approach, and our results show that the obtained 5-days menus using the proposed methodology not only comply with the restrictions imposed by the authorities but also produce inexpensive and healthy menus. We also appraise the software itself using an opinion pool among nine nutritionists. The professionals considered our software above expectations.

Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy

In this work, we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta (ρ1,ρ2). The Deployment Delta (ρ1,ρ2) is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare GRASP+VNS to some baseline algorithms: (i) Delta-g; (ii) Delta-r and, (iii) the optimal value. Our results demonstrate that our approach requires up to 90% less Roadside Units to meet the QoS required by Deployment Delta (ρ1,ρ2) metric. Besides, different from the baseline algorithms, our approach find results that differ no more than 17% from the optimal values for all tested instances.

A Multiobjective Strategy to Allocate Roadside Units in a Vehicular Network with Guaranteed Levels of Service

In this work, we propose the Delta-MGA, a specific multiobjective algorithm for solving the allocation of Roadside Units RSUs in a Vehicular Network VANETs. We propose two multiobjective models to solve two different problems. The first one, our objectives are to find the minimum set of RSUs and to maximize the number of covered vehicles. The second one, our objectives are to find the minimum set of RSUs and to maximize the percentage of time that each vehicle remains connected. Our metric is based on Delta Network metric proposed in literature. As far as we concerned, Delta-MGA is the first multiobjective approach to present a deployment strategy for VANETs. We compare our approach with two mono-objective algorithms: i Delta-r; ii Delta-GA. Our results demonstrate that our approach gets better results when compared with Delta-r algorithm and competitive results when compared with Delta-GA algorithm. Furthermore, the main advantage of Delta-MGA algorithm is that with it is possible to find several different solutions given to the planning authorities diverse alternatives to deploy the RSUs.