Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling

In decision theory, the weighted sum model (WSM) is the best known Multi-Criteria Decision Analysis (MCDA) approach for evaluating a number of alternatives in terms of a number of decision criteria. Assigning weights is a difficult task, especially if the number of criteria is large and the criteria are very different in character. There are some problems in the real world which utilize conflicting criteria and mutual effect. In the field of automotive, the knocking phenomenon in internal combustion or spark ignition engines limits the efficiency of the engine. Power and fuel economy can be maximized by optimizing some factors that affect the knocking phenomenon, such as temperature, throttle position sensor, spark ignition timing, and revolution per minute. Detecting knocks and controlling the above factors or criteria may allow the engine to run at the best power and fuel economy. The best decision must arise from selecting the optimum trade-off within the above criteria. The main objective of this study was to proposed a new Non-Weighted Aggregate Evaluation Function (NWAEF) model for non-linear multi-objectives function which will simulate the engine knock behavior (non-linear dependent variable) in order to optimize non-linear decision factors (non-linear independent variables). This study has focused on the construction of a NWAEF model by using a curve fitting technique and partial derivatives. It also aims to optimize the nonlinear nature of the factors by using Genetic Algorithm (GA) as well as investigate the behavior of such function. This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. The Akaike Information Criterion (AIC) is used to balance the complexity of the model and the data loss, which can help assess the range of the tested models and choose the best ones. Some statistical tools are also used in this thesis to assess and identify the most powerful explanation in the model. The first derivative is used to simplify the form of evaluation function. The NWAEF model was compared to Random Weights Genetic Algorithm (RWGA) model by using five data sets taken from different internal combustion engines. There was a relatively large variation in elapsed time to get to the best solution between the two model. Experimental results in application aspect (Internal combustion engines) show that the new model participates in decreasing the elapsed time. This research provides a form of knock control within the subspace that can enhance the efficiency and performance of the engine, improve fuel economy, and reduce regulated emissions and pollution. Combined with new concepts in the engine design, this model can be used for improving the control strategies and providing accurate information to the Engine Control Unit (ECU), which will control the knock faster and ensure the perfect condition of the engine

Dual level searching approach for solving multi-objective optimisation problems using hybrid particle swarm optimisation and bats echolocation-inspired algorithms

A dual level searching approach for multi objective optimisation problems using particle swarm optimisation and modified adaptive bats sonar algorithm is presented. The concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is integrated with the established particle swarm optimisation algorithm. The proposed algorithm incorporates advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handle the complexity of multi objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi objective optimisation benchmark test functions and problem. The acquired results show that the proposed algorithm perform well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving of multi objective optimisation problems

Multi-objective analysis of performance in mileage based on the minimization of several vehicle polluting features

This paper analyses the fuel consumption and emissions of and from various kinds of automobiles. The aim is to identify the set of automobiles models that produce the least pollution and provide the higher mileage. To complete the analysis, a multi-objective optimization problem (MOP) has been proposed with a visual representation methodology of the Pareto front (Level Diagram); in this way, it has been determined that the highest compromise values corresponding to the utopian point determine a mileage performance of 16.30 [km/l]. Finally, it is important to highlight that the MOP has facilitated the analysis process, which helps the Decision Maker (DM) in the adequate selection of the final solution, based on the available knowledge of the set of optimal solutions

Algoritmos evolutivos para optimización multiobjetivo: un estudio comparativo en un ambiente paralelo asíncrono

El presente trabajo propone el desarrollo de Algoritmos Evolutivos Multiobjetivos paralelos (parallel Multi-Objective Evolutionary Algorithms- pMOEAs). Varios modelos de paralelización alternativos fueron considerados y analizados. Siguiendo distintos modelos propuestos, diferentes MOEAs han sido implementados en paralelo y aplicados en la resolución de problemas de prueba de distinta dificultad. Los resultados obtenidos por los distintos MOEAs, tanto en sus versiones secuenciales como paralelas, han sido comparados y analizados en base a distintas métricas experimentales de desempeño. La paralelización de MOEAs ha demostrado ser una alternativa válida para mejorar el desempeño de los estos algoritmos en todos los problemas de prueba considerados.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Algoritmos evolutivos para optimización multiobjetivo: un estudio comparativo en un ambiente paralelo asíncrono

El presente trabajo propone el desarrollo de Algoritmos Evolutivos Multiobjetivos paralelos (parallel Multi-Objective Evolutionary Algorithms- pMOEAs). Varios modelos de paralelización alternativos fueron considerados y analizados. Siguiendo distintos modelos propuestos, diferentes MOEAs han sido implementados en paralelo y aplicados en la resolución de problemas de prueba de distinta dificultad. Los resultados obtenidos por los distintos MOEAs, tanto en sus versiones secuenciales como paralelas, han sido comparados y analizados en base a distintas métricas experimentales de desempeño. La paralelización de MOEAs ha demostrado ser una alternativa válida para mejorar el desempeño de los estos algoritmos en todos los problemas de prueba considerados.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Comparison of multi-objective optimization methodologies for engineering applications

Computational models describing the behavior of complex physical systems are often used in the engineering design field to identify better or optimal solutions with respect to previously defined performance criteria. Multi-objective optimization problems arise and the set of optimal compromise solutions (Pareto front) has to be identified by an effective and complete search procedure in order to let the decision maker, the designer, to carry out the best choice. Four multi-objective optimization techniques are analyzed by describing their formulation, advantages and disadvantages. The effectiveness of the selected techniques for engineering design purposes is verified by comparing the results obtained by solving a few benchmarks and a real structural engineering problem concerning an engine bracket of a ca

An evaluation framework to support optimisation of scenarios for energy efficient retrofitting of buildings at the district level

Producción CientíficaEnergy-efficient retrofitting of buildings has become essential to achieve the environmental objectives of the European Union’s (EU) strategies towards reducing carbon emissions and energy dependency on fossil fuels. When tackling retrofitting projects, the issue of scale becomes essential as sometimes this can determine the sustainability of the project. Therefore, a comprehensive approach is essential to ensure effective decision-making. A platform has been designed within the EU funded OptEEmAL project to support stakeholders in this process, providing functionalities that can automatically model and evaluate candidate retrofitting alternatives considering their priorities, targets and boundary conditions. A core element of this platform is the evaluation framework deployed which implements a multi-criteria decision-making approach to transform the priorities of stakeholders into quantifiable weights used to compare the alternatives. As a result, more informed decisions can be made by the stakeholders through a comprehensive evaluation of the candidate retrofitting scenarios. This paper presents the approach followed to develop and integrate this evaluation framework within the platform as well as its validation in a controlled environment to ensure its effectiveness

Multi-objective optimization using statistical models

In this paper we consider multi-objective optimization problems (MOOP) from the point of view of Bayesian analysis. MOOP problems can be considered equivalent to certain statistical models associated with the specific objectives and constraints. MOOP that can explore accurately the Pareto frontier are Generalized Data Envelopment Analysis and Goal Programming. In turn, posterior analysis of their associated statistical models can be implemented using Markov Chain Monte Carlo (MCMC) simulation. In addition, we consider the minimax regret problem which provides robust solutions and we develop similar MCMC posterior simulators without the need to define scenarios. The new techniques are shown to work well in four examples involving non-convex and disconnected Pareto problems and to a real world portfolio optimization problem where the purpose is to optimize simultaneously average return, mean absolute deviation, positive and negative skewness of portfolio returns. Globally minimum regret can also be implemented based on post-processing of MCMC draws. © 2019 Elsevier B.V