Search results for " Multiobjective Optimization"
showing 6 items of 16 documents
Multiobjective Optimal Reconfiguration of MV Networks with Different Earthing Systems
2010
The paper deals with the traditional problem of multiobjective optimal reconfiguration applied to power distribution systems considering the safety issue in the formulation. The applications are devoted to the solution of the posed problem in networks in which coexist energy sources with unearthed neutral point and resonant earthed neutral point. After a brief review of the most recent papers on optimal reconfiguration, the paper outlines the safety problem and provides a solution to the multiobjective problem using the Non dominated Sorting Genetic Algorithm II aiming at: minimal power losses operation, safety check at distribution substations and load balancing among the HV/MV transformer…
Feature selection: A multi-objective stochastic optimization approach
2020
The feature subset task can be cast as a multiobjective discrete optimization problem. In this work, we study the search algorithm component of a feature subset selection method. We propose an algorithm based on the threshold accepting method, extended to the multi-objective framework by an appropriate definition of the acceptance rule. The method is used in the task of identifying relevant subsets of features in a Web bot recognition problem, where automated software agents on the Web are identified by analyzing the stream of HTTP requests to a Web server.
Towards resilient cities: Advancements allowed by a multi-criteria optimization tool to face the new challenges of European Union’s climate and energ…
2019
Abstract The United Nations as well as the European Union are strongly committed in promoting a transition towards more sustainable and resilient cities. Indeed, they are increasingly affected by different types of threats, among which the natural ones such as earthquakes, fires, and floods (shocks) and climate variability (stresses). Cities are quite often unable to cope with the adverse effects of such natural hazards. This circumstance leads to the need of introducing resilience-related criteria (besides commonly used sustainability indicators) in decision-making processes. This paper investigates at which extent the inclusion of such new indicators, within multi-criteria assessment tool…
Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study
2014
Dynamic process simulators for plant-wide process simulation and multiobjective optimization tools can be used by industries as a means to cut costs and enhance profitability. Specifically, dynamic process simulators are useful in the process plant design phase, as they provide several benefits such as savings in time and costs. On the other hand, multiobjective optimization tools are useful in obtaining the best possible process designs when multiple conflicting objectives are to be optimized simultaneously. Here we concentrate on interactive multiobjective optimization. When multiobjective optimization methods are used in process design, they need an access to dynamic process simulators, …
Comparing reference point based interactive multiobjective optimization methods without a human decision maker
2022
AbstractInteractive multiobjective optimization methods have proven promising in solving optimization problems with conflicting objectives since they iteratively incorporate preference information of a decision maker in the search for the most preferred solution. To find the appropriate interactive method for various needs involves analysis of the strengths and weaknesses. However, extensive analysis with human decision makers may be too costly and for that reason, we propose an artificial decision maker to compare a class of popular interactive multiobjective optimization methods, i.e., reference point based methods. Without involving any human decision makers, the artificial decision make…
On Using Decision Maker Preferences with ParEGO
2017
In this paper, an interactive version of the ParEGO algorithm is introduced for identifying most preferred solutions for computationally expensive multiobjective optimization problems. It enables a decision maker to guide the search with her preferences and change them in case new insight is gained about the feasibility of the preferences. At each interaction, the decision maker is shown a subset of non-dominated solutions and she is assumed to provide her preferences in the form of preferred ranges for each objective. Internally, the algorithm samples reference points within the hyperbox defined by the preferred ranges in the objective space and uses a DACE model to approximate an achievem…