Comparando Algoritmos Evolutivos Baseados em Decomposição para Problemas de Otimização Multiobjetivo e com Muitos Objetivos

Many real-world problems can be mathematically modeled as Multiobjective Optimization Problems (MOPs), as they involve multiple conflicting objective functions that must be minimized simultaneously. MOPs with more than 3 objective functions are called Many-objective Optimization Problems (MaOPs). MOPs are typically solved through Multiobjective Evolutionary Algorithms (MOEAs), which can obtain a set of non-dominated optimal solutions, known as a Pareto front, in a single run. The MOEA Based on Decomposition (MOEA/D) is one of the most efficient, dividing a MOP into several single-objective subproblems and optimizing them simultaneously. This study evaluated the performance of MOEA/D and four variants representing the state of the art in the literature (MOEA/DD, MOEA/D-DE, MOEA/D-DU, and MOEA/D-AWA) in MOPs and MaOPs. Computational experiments were conducted using benchmark MOPs and MaOPs from the DTLZ suite considering 3 and 5 objective functions. Additionally, a statistical analysis, including the Wilcoxon test, was performed to evaluate the results obtained in the IGD+ performance indicator. The Hypervolume performance indicator was also considered in the combined Pareto front, formed by all solutions obtained by each MOEA. The experiments revealed that MOEA/DD performed best in IGD+, and MOEA/D-AWA achieved the highest Hypervolume in the combined Pareto front, while MOEA/D-DE registered the worst result in this set of problems.Muitos problemas oriundos do mundo real podem ser modelados matematicamente como Problemas de Otimização Multiobjetivo (POMs), já que possuem diversas funções objetivo conflitantes entre si que devem ser minimizadas simultaneamente. POMs com mais de 3 funções objetivo recebem o nome de Problemas de Otimização com Muitos Objetivos (MaOPs, do inglês Many-objective Optimization Problems). Os POMs geralmente são resolvidos através de Algoritmos Evolutivos Multiobjetivos (MOEAs, do inglês Multiobjective Evolutionary Algorithms), os quais conseguem obter um conjunto de soluções ótimas não dominadas entre si, conhecidos como frente de Pareto, em uma única execução. O MOEA baseado em decomposição (MOEA/D) é um dos mais eficientes, o qual divide um POM em vários subproblemas monobjetivos otimizando-os ao mesmo tempo. Neste estudo foi realizada uma avaliação dos desempenhos do MOEA/D e quatro de suas variantes que representam o estado da arte da literatura (MOEA/DD, MOEA/D-DE, MOEA/D-DU e MOEA/D-AWA) em POMs e MaOPs. Foram conduzidos experimentos computacionais utilizando POMs e MaOPs benchmark do suite DTLZ considerando 3 e 5 funções objetivo. Além disso, foi realizada uma análise estatística que incluiu o teste de Wilcoxon para avaliar os resultados obtidos no indicador de desempenho IGD+. Também foi considerado o indicador de desempenho Hypervolume na frente de Pareto combinada, que é formada por todas as soluções obtidas por cada MOEA. Os experimentos revelaram que o MOEA/DD apresentou a melhor performance no IGD+ e o MOEA/D-AWA obteve o maior Hypervolume na frente de Pareto combinada, enquanto o MOEA/D-DE registrou o pior resultado nesse conjunto de problemas

The dance of classes:a stochastic model for software structure evolution

In this study, we investigate software structure evolution and growth. We represent software structure by means of a generic macro-topology called Little House, which models the dependencies among classes of object-oriented software systems. We, then, define a stochastic model to predict the way software architectures evolve. The model estimates how the classes of object-oriented programs get connected one to another along the evolution of the systems. To define the model, we analyzed data from 81 versions of six Java based projects. We analyzed each pair of sequential versions, for each project, in order to depict a pattern of software structure evolution based on Little House. To evaluate the model, we performed two experiments: one with the data used to derive the model, and another with data of 35 releases, in total, of four open-source Java project. In both experiments, we found a very low rate of error for the application of the proposed model. The evaluation of the model suggests it is able to predict how a software structure will evolve

Real-polarized genetic algorithm for the three-dimensional bin packing problem

This article presents a non-deterministic approach to the Three-Dimensional Bin Packing Problem, using a genetic algorithm. To perform the packing, an algorithm was developed considering rotations, size constraints of objects and better utilization of previous free spaces (flexible width). Genetic operators have been implemented based on existing operators, but the highlight is the Real-Polarized crossover operator that produces new solutions with a certain disturbance near the best parent. The proposal presented here has been tested on instances already known in the literature and real instances. A visual comparison using boxplot was done and, in some situations, it was possible to say that the obtained results are statistically superior than the ones presented in the literature. In a given instance class, the presented Genetic Algorithm found solutions reaching up to 70% less bins

The menu planning problem:a multi-objective approach for the Brazilian schools context

In this work, we developed a genetic algorithm for solving the automatic menu planning for the Brazilian school context. Our objectives are to create menus that: (i) minimize the total cost and, simultaneously, (ii) minimize the nutritional error according to the Brazilian reference. Those menus also satisfy requirements of the Brazilian government, for example: (i) student age group, (ii) school category, (iii) school duration time, (iv) school location, (v) variety of preparations, (vi) harmony of preparations and, (vii) maximum amount to be paid for each meal. To tackle this problem, we transformed our multiobjective in a mono-objective problem using the linear scalarization method and solved it with a genetic algorithm. We also developed a multiobjective algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II). Our results demonstrate that the multiobjective approach is 5 times faster, with 30 times more non-dominated solutions and give solutions that are statistically better compared with the mono-objective algorithm. Another advantage of this the approach is the diversity of solutions, allowing the professional (nutritionist) choose one among the various menus obtained by the algorithm, giving priority to the objective that is considered to be the most relevant in a given situation

Evaluating the use of a Net-Metering mechanism in microgrids to reducepower generation costs with a swarm-intelligent algorithm

The micro-generation of electricity arises as a clean and efficient alternative to provide electrical power. However, the unpredictability of wind and solar radiation poses a challenge to attend load demand, while maintaining a stable operation of the microgrids (MGs). This paper proposes the modeling and optimization, using a swarm-intelligent algorithm, of a hybrid MG system (HMGS) with a Net-Metering compensation policy. Using real industrial and residential data from a Spanish region, a HMGS with a generic ESS is used to analyze the influence of four different Net-Metering compensation levels regarding costs, percentage of renewable energy sources (RESs), and LOLP. Furthermore, the performance of two ESSs, Lithium Titanate Spinel (Li4Ti5O12 (LTO)) and Vanadium redox flow batteries (VRFB), is assessed in terms of the final $/kWh costs provided by the MG. The results obtained indicate that the Net-Metering policy reduces the surplus from over 14$ dollars of savings, when compared to a MG using a LTO system without Net-Metering.European CommissionAgencia Estatal de InvestigaciónComunidad de Madri

CardNutri: software of weekly menus nutritional planning for scholar feeding applying artificial intelligence

The aim of this paper is to present and evaluate a software that uses Artificial Intelligence techniques to design, automatically and quickly, weekly nutritional menus for School Feeding, meeting the daily nutritional needs of students and simultaneously minimizing the total cost of the menu. These menus meet the nutritional references the National School Feeding Programme (PNAE) according to age, variety, the harmony of preparations and a maximum amount to be paid per meal. The response time for this preparation does not exceed five minutes. However, the nutritionist must choose the menu that suits you best, because the tool provides a set of efficient menus. Thus, the system contributes to the development of nutritious and cheap menus, in addition to facilitating nutritionist work spends much time for this task, since it needs to perform other duties of responsibilit

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system ? a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.European CommissionAgencia Estatal de InvestigaciónComunidad de Madri

A wavelet-based sampling algorithm for wireless sensor networks applications.

This work proposes and evaluates a sampling algorithm based on wavelet transforms with Coiflets basis to reduce the data sensed in wireless sensor networks applications. The Coiflets basis is more computationally efficient when data are smooth, which means that, data are well approximated by a polynomial function. As expected, this algorithm reduces the data traffic in wireless sensor network and, consequently, decreases the energy consumption and the de-lay to delivery the sensed information. The main contribution of this algorithm is the capability to detect some event by adjusting the sampling dynamically. In order to evaluate the algorithm, we compare it with a static sampling strategy considering a real sens-ing data where an external event is simulated. The results reveal the efficiency of the proposed method by reducing the data with-out loosing its representativeness, including when some event oc-curs. This algorithm can be very useful to design energy-efficient and time-constrained sensor networks when it is necessary to detect some event