Catálogo de publicaciones - libros

The work in this paper presents the use of population-based incremental learning (PBIL), one of the classic single-objective population-based optimisation methods, as a tool for multiobjective optimisation. The PBIL method with two different updating schemes of its probability vectors is presented. The performance of the two proposed multiobjective optimisers are measured and compared with four other established multiobjective evolutionary algorithms i.e. niched Pareto genetic algorithm, version 2 of non-dominated sorting genetic algorithm, version 2 of strength Pareto evolutionary algorithm, and Pareto archived evolution strategy. The optimisation methods are implemented to solve 8 bi-objective test problems where design variables are encoded as a binary string. The Pareto optimal solutions obtained from the various methods are compared and discussed. It can be concluded that, with the assigned test problems, the multiobjective PBIL methods are comparable to the previously developed algorithms in terms of convergence rate. The clear advantage in using PBILs is that they can provide considerably better population diversity.

Differential evolution (DE) is a population-based and stochastic search algorithm of evolutionary computation that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. This work presents a global optimization algorithm based on DE approaches combined with local search using the implicit filtering algorithm. The implicit filtering algorithm is a projected quasi-Newton method that uses finite difference gradients. The difference increment is reduced as the optimization progresses, thereby avoiding some local minima, discontinuities, or nonsmooth regions that would trap a conventional gradient-based method. Problems involving optimization procedures of complex mathematical functions are widespread in electromagnetics. Many problems in this area can be described by nonlinear relationships, which introduce the possibility of multiple local minima. In this paper, the shape design of Loney’s solenoid benchmark problem is carried out by DE approaches. The results of DE approaches are also investigated and their performance compared with those reported in the literature.

A layered matrix encoding cascade genetic algorithm and particle swarm optimization approach (GA-PSO) for unit commitment and economic load dispatch problem in a thermal power system is presented in this paper. The tasks of determining and allocating power generation to different thermal units in a way that the total power production cost is at the minimum subject to equality and inequality constraints makes unit commitment and economic load dispatch challenging. A case study, based on the thermal power generation problem presented in [1], is used to demonstrate the effectiveness of the proposed method in generating a cost-effective power generation schedule. The schedule obtained is compared with that of Linear Programming (LP) as reported in [1]. The results show that the proposed GA-PSO approach outperforms LP in solving the unit commitment and economic load dispatch problem for thermal power generation system in this case study.

Differential Evolution (DE) is a competitive optimization technique for numerical optimization problems with real-parameter representation. This paper aims to investigate how DE can be adapted with binary encoding and to study its behaviors on the binary level.

Effective pavement management requires the prioritization of the road stretches for logical disbursement of the funds available towards maintenance of the pavement. Several methods have been developed and implemented towards this goal. However, the uncertainty involved with some of the parameters has not been addressed adequately in most of the works. One such parameter has been identified as the severity of distress which is difficult to assess accurately. Hence a Fuzzy Multi Criteria Decision Making (FMCDM) approach has been proposed in this paper. For demonstration of the approach, pavement distresses with respect to their extent and severity have been collected over a number of stretches. In addition, an expert opinion survey has been carried out to quantify the influence of these parameters on the functional condition of the pavement. Priority Index (PI) has been worked out, based on which the ranking of the stretches has been arrived at.

The majority of real optimization problems cannot be solved exactly because they have very large and highly complex search spaces. One of these complex problems is the design of looped water distribution networks, which consists of determining the best way of conveying water from the sources to the users, satisfying their requirements. This paper is to present a new memetic algorithm and evaluate its performance in this problem. With the aim to establish an accurate conclusion, other four heuristic approaches have also been adapted, including simulated annealing, mixed simulated annealing and tabu search, iterated local search, and scatter search. Results obtained in two water distribution networks demonstrate that the memetic algorithm works better when the size of the problem increases.

The present paper aims to find neural network based air quality predictors, which can work with limited number of data sets and are robust enough to handle data with noise and errors. A number of available variations of neural network models such as Recurrent Network Model (RNM), Change Point detection Model with RNM (CPDM), Sequential Network Construction Model (SNCM), and Self Organizing Feature Maps (SOFM) are implemented for predicting air quality. Developed models are applied to simulate and forecast based on the long-term (annual) and short-term (daily) data. The models, in general, could predict air quality patterns with modest accuracy. However, SOFM model performed extremely well in comparison to other models for predicting long-term (annual) data as well as short-term (daily) data.

This research presents an investigation into a new population inheritance approach using a concept taken from the recessive trait idea for evolutionary optimization. Evolutionary human inheritance recessive trait idea is used to enhance the effectiveness of the traditional genetic algorithms. The capability of the modified approach is explored by two examples (i) a mathematical function of two variables, and (ii) an active vibration control of a flexible beam system. Finally, a comparative performance for convergence is presented and discussed to demonstrate the merits of the modified genetic algorithms approach over the traditional ones.

An automated neural network-based system for predicting solar flares from their associated sunspots and simulated solar cycle is introduced. A sunspot is the cooler region of the Sun’s photosphere which, thus, appears dark on the Sun’s disc, and a solar flare is sudden, short lived, burst of energy on the Sun’s surface, lasting from minutes to hours. The system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots and flares. Size, shape and spot density of relevant sunspots are used as input values, in addition to the values found by the solar activity model introduced by Hathaway. Two outputs are provided: The first is a flare/ no flare prediction, while the second is type of the solar flare prediction (X or M type flare). Our system provides 91.7% correct prediction for the possible occurrences and, 88.3% correct prediction for the type of the solar flares.