Eduardo G. Carrano

A dynamic multiobjective hybrid approach for designing Wireless Sensor Networks

The increase in the demand for Wireless Sensor Networks (WSNs) has intensified studies which aim to obtain energy-efficient solutions, since the energy storage limitation is critical in those systems. However, there are other aspects which usually must be ensured in order to provide an efficient design of WSNs, such as area coverage and network connectivity. This paper proposes a multiobjective hybrid approach for solving the Dynamic Coverage and Connectivity Problem (DCCP) in flat WSN subjected to node failures. It combines a multiobjective global on-demand algorithm (MGoDA), which improves the current DCCP solution using a Genetic Algorithm, with a local online algorithm (LoA), which is intended to restore the network coverage when one or more failures occur. The proposed approach is compared with an Integer Linear Programming (ILP) based approach and a similar mono-objective approach with regard to coverage, energy consumption and residual energy of the solution provided by each method. Results achieved for a test instance show that the hybrid approach presented can obtain good solutions with a considerably smaller computational cost than ILP. The multiobjective approach still provides a feasible method for extending WSNs lifetime with slight decreasing in the network mean coverage.

On a stochastic differential equation approach for multiobjective optimization up to pareto-criticality

Traditional Evolutionary Multiobjective Optimization techniques, based on derivative-free dominance-based search, allowed the construction of efficient algorithms that work on rather arbitrary functions, leading to Pareto-set sample estimates obtained in a single algorithm run, covering large portions of the Pareto-set. However, these solutions hardly reach the exact Pareto-set, which means that Pareto-optimality conditions do not hold on them. Also, in problems with high-dimensional objective spaces, the dominance-based search techniques lose their efficiency, up to situations in which no useful solution is found. In this paper, it is shown that both effects have a common geometric structure. A gradient-based descent technique, which relies on the solution of a certain stochastic differential equation, is combined with a multiobjective line-search descent technique, leading to an algorithm that indicates a systematic solution for such problems. This algorithm is intended to serve as a proof of concept, allowing the comparison of the properties of the gradient-search principle with the dominance-search principle. It is shown that the gradient-based principle can be used to find solutions which are truly Pareto-critical, satisfying first-order conditions for Pareto-optimality, even for many-objective problems.

An enhanced statistical approach for evolutionary algorithm comparison

This paper presents an enhanced approach for comparing evolutionary algorithm. This approach is based on three statistical techniques: (a) Principal Component Analysis, which is used to make the data uncorrelated; (b) Bootstrapping, which is employed to build the probability distribution function of the merit functions; and (c) Stochastic Dominance Analysis, that is employed to make possible the comparison between two or more probability distribution functions. Since the approach proposed here is not based on parametric properties, it can be applied to compare any kind of quantity, regardless the probability distribution function. The results achieved by the proposed approach have provided more supported decisions than former approaches, when applied to the same problems.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multiobjective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

Feedback-Control Operators for Evolutionary Multiobjective Optimization

New operators for Multi-Objective Evolutionary Algorithms (MOEAs) are presented here, including one archive-set reduction procedure and two mutation operators, one of them to be applied on the population and the other one on the archive set. Such operators are based on the assignment of spheres to the points in the objective space, with the interpretation of a representative region. The main contribution of this work is the employment of feedback control principles (PI control) within the archive-set reduction procedure and the archive-set mutation operator, in order to achieve a well-distributed Pareto-set solution sample. An example EMOA is presented, in order to illustrate the effect of the proposed operators. The dynamic effect of the feedback control scheme is shown to explain a high performance of this algorithm in the task of Pareto-set covering.

Continuous-space embedding genetic algorithm applied to the Degree Constrained Minimum Spanning Tree Problem

This work presents an evolutionary approach for solving a difficult problem of combinatorial optimization, the DCMST (Degree-Constrained Minimum Spanning Tree Problem). Three genetic algorithms which embed candidate solutions in the continuous space [1] are proposed here for solving the DCMST. The results achieved by these three algorithms have been compared with four other existing algorithms according to three merit criteria: i) quality of the best solution found; ii) computational effort spent by the algorithm, and; iii) convergence tendency of the population. The three proposed algorithms have provided better results for both solution quality and population convergence, with reasonable computational cost, in tests performed for 25-node and 50-node test instances. The results suggest that the proposed algorithms are well suited for dealing with the problem under study.

A new memory based variable-length encoding genetic algorithm for multiobjective optimization

This paper presents a new memory-based variable-length encoding genetic algorithm for solving multiobjective optimization problems. The proposed method is a binary implementation of the NSGA2 and it uses a Hash Table for storing all the solutions visited during algorithm evolution. This data structure makes possible to avoid the re-visitation of solutions and it provides recovering and storage of data with low computational cost. The algorithm memory is used for building crossover, mutation and local search operators with a parameterless variable-length encoding. These operators control the neighborhood based on the density of points already visited on the region of the new solution to be evaluated. Two classical multiobjective problems are used to compare two variations of the proposed algorithm and two variations of the binary NSGA2. A statistical analysis of the results indicates that the memory-based adaptive neighborhood operators are able to provide significant improvement of the quality of the Pareto-set approximations.

Hybrid multiobjective approach for designing wireless sensor networks

The increasing demand for Wireless Sensor Networks (WSN) has intensified studies which aim to obtain energy-efficient solutions, since the energy storage limitation is critical in those systems. However, 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 network performance enhancement: a multiobjective hybrid approach for solving the Dynamic Coverage and Connectivity Problem in flat WSN subjected to node failures.Results achieved for a test instance show that the hybrid approach can improve the performance of the WSN obtaining good solutions with a considerably smaller computational cost than ILP.

Semi-supervised training of Least Squares Support Vector Machine using a multiobjective evolutionary algorithm

Support Vector Machines (SVMs) are considered state-of-the-art learning machines techniques for classification problems. This paper studies the training of SVMs in the special case of problems in which the raw data to be used for training purposes is composed of both labeled and unlabeled data - the semi-supervised learning problem. This paper proposes the definition of an intermediate problem of attributing labels to the unlabeled data as a multiobjective optimization problem, with the conflicting objectives of minimizing the classification error over the training data set and maximizing the regularity of the resulting classifier. This intermediate problem is solved using an evolutionary multiobjective algorithm, the SPEA2. Simulation results are presented in order to illustrate the suitability of the proposed technique.

Designing a multilayer microwave heating device using a multiobjective genetic algorithm

In this paper, we propose a multiobjective evolutionary approach to design a microwave heating device. The goal is to heat the maximum amount of water, above certain temperature, and spending the minimum energy. The device is modeled as a loss multilayer dielectric irradiated by microwave power. The resulting bi-objective problem is then solved using SPEA2 and a set of solutions is obtained. The results show that SPEA2 finds a higher number of non-dominated solution when compared with the traditional approaches used in this problem, within lower computational cost.