Ricardo H. C. Takahashi

Improvements in genetic algorithms

This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAs are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.

Electric distribution network multiobjective design using a problem-specific genetic algorithm

This paper presents a multiobjective approach for the design of electrical distribution networks. The objectives are defined as a monetary cost index (including installation cost and energy losses cost) and a system failure index. The true Pareto-optimal solutions are found with a multiobjective genetic algorithm that employs an efficient variable encoding scheme and some problem-specific mutation and crossover operators. Results based on 21- and 100-bus systems are presented. The information gained from the Pareto-optimal solution set is shown to be useful for the decision-making stage of distribution network evolution planning.

Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties

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.

Improving generalization of MLPs with multi-objective optimization

Abstract This paper presents a new learning scheme for improving generalization of multilayer perceptrons. The algorithm uses a multi-objective optimization approach to balance between the error of the training data and the norm of network weight vectors to avoid overfitting. The results are compared with support vector machines and standard backpropagation.

A genetic algorithm for irregularly shaped spatial scan statistics

A new approach is presented for the detection and inference of irregularly shaped spatial clusters, using a genetic algorithm. Given a map divided into regions with corresponding populations at risk and cases, the graph-related operations are minimized by means of a fast offspring generation and efficient evaluation of Kuldorffs spatial scan statistic. A penalty function based on the geometric non-compactness concept is employed to avoid excessive irregularity of cluster geometric shape. The algorithm is an order of magnitude faster and exhibits less variance compared to the simulated annealing scan, and is more flexible than the elliptic scan. It has about the same power of detection as the simulated annealing scan for mildly irregular clusters and is superior for the very irregular ones. An application to breast cancer clusters in Brazil is discussed.

A multiobjective methodology for evaluating genetic operators

This paper is concerned with the problem of evaluating genetic algorithm (GA) operator combinations. Each GA operator, like crossover or mutation, can be implemented according to several different formulations. This paper shows that: 1) the performances of different operators are not independent and 2) different merit figures for measuring a GA performance are conflicting. In order to account for this problem structure, a multiobjective analysis methodology is proposed. This methodology is employed for the evaluation of a new crossover operator (real-biased crossover) that is shown to bring a performance enhancement. A GA that was found by the proposed methodology is applied in an electromagnetic (EM) benchmark problem.

Multiobjective H/sub 2//H/sub /spl infin// guaranteed cost PID design

This article presents a methodology for sub-optimal design of PID compensators for systems subject to disturbance signals and to parametric uncertainties of polytope type. The adopted optimality criteria are the H/sub 2/ and H/sub /spl infin// norms of the transfer matrices from the disturbance inputs and from the reference input to the controlled output error. Time constant constraints are also employed in the optimization procedure. The PID parameter selection combines the different optimization criteria through a multiobjective technique. True guaranteed cost values for optimization criteria are calculated. An example is presented, showing the uncertainty polytope construction from physical parameters tolerances and the PID synthesis procedure. A genetic algorithm and extensive simulations are employed in order to evaluate the proposed algorithm performance.

Incipient fault detection in induction machine stator-winding using a fuzzy-Bayesian change point detection approach

In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network classification algorithm that defines the model to be used, one change point or two change points. The second step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that can be approximated by a beta distribution. The fuzzy cluster centers are determined by using the Kohonen neural network classification algorithm used in the first step. The last step consists in using the Metropolis-Hastings algorithm for performing the change point detection in the transformed time series generated by the second step with that known distribution. The incipient faults are detected as long as they characterize change points in such transformed time series. The main contribution of the proposed approach in this paper, related to previous one in the Literature, is to detect up to two change points in the time series considered, besides the enhanced resilience of the new fault detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation results are presented to illustrate the proposed methodology.

Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria

This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems.