Search results for " Multi-objective"

A New Meta-Heuristic Multi-Objective Approach For Optimal Dispatch of Dispersed and Renewable Generating Units in Power Distribution Systems

2011

The application of stochastic methods in engineering research and optimization has been increasing over the past few decades. Ant Colony Optimization, in particular, has been attracting growing attention as a promising approach both in discrete and continuous domains. The present work proposes a multi-objective Ant Colony Optimization for continuous domains showing good convergence properties and uniform coverage of the non-dominated front. These properties have been proved both with mathematical test functions and with a complex real world problem. Besides the second part of the chapter presents the application of the new algorithm to the problem of optimal dispatch of dispersed power gene…

A multi-objective approach to solid waste management

2010

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached i…

Multi-modal search for multiobjective optimization: an application to optimal smart grids management

2012

This paper studies the possibility to use efficient multimodal optimizers for multi-objective optimization. In this paper, the application area considered for such new approach is the optimal dispatch of energy sources in smart microgrids. The problem indeed shows a non uniform Pareto front and requires efficient optimal search methods. The idea is to exploit the potential of agents in population-based heuristics to improve diversity in the Pareto front, where solutions show the same rank and are thus equally weighted. Since Pareto dominance is at the basis of the theory of multi-objective optimization, most algorithms show the non dominance ranking as quality indicator, with some problem i…

Hybrid evolutionary multi-objective optimization with enhanced convergence and diversity

2011

Optimal design of k-out-of-n systems

2016

The present paper is aimed at finding the best compromise to design a system k-out-of-n reliability configuration by means of the formulation of a multi-objective mathematical model. The Pareto front, which is the set of non-dominated solutions, is determined by considering the stationary availability and the achievable profit as objectives to be simultaneously optimized. The Pareto front represents a useful tool for the analyst to make the choice related to the analyzed design problem. In addition, the knowledge of the Pareto front permits to consider different design scenarios.

Multi-objective parameter identification via ACOR algorithm

2009

The spreading of advanced constituive models, needed to model complex phenomena, makes necessary to solve difficult parameter identification problems. The need of multiple tests to fully characterize the experimental behaviour makes the parameter identification problem a multi objective one. Unlike conventional techniques, based on the formulation of an aggregate scalar ob- jective function, in the present work the problem is addressed using a new multi objective algorithm obtained extending the continuous Ant Colony Optimization algorithm. Mathematical tests and ap- plication to a real world problem are performed and different performance measures are used to asses the performance of the a…

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

2015

Classical evolutionary multi-objective optimization algorithms aim at finding an approx- imation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimiza- tion problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provid…