In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intelligent generation mechanism (MOIGM) is proposed to efficiently solve multi-objective optimization problems with large parameters. Instead of generate-and-test methods, MOIGM makes use of a systematic reasoning ability ...

Abstract We study the problem of aggregating discounted utility preferences into a social preference model. use an axiom capturing responsibility individuals’ attitudes to time, called consensus Pareto. show that this can provide consistent foundations for welfare judgments. Moreover, in conjunction with standard axioms anonymity and continuity, Pareto help adjudicate some fundamental issues re...

This paper uses the tools and techniques of generalized expected utility analysis to explore the robustness of some of the classical basic results in insurance theory to departures from the expected utility hypothesis on agents' risk preferences. The areas explored consist of individual demand for coinsurance and deductible insurance, the structure of Pareto-efficient bilateral insurance contra...

We introduce a hierarchical Gauss-Pareto model for spatial prediction of 24 hour cumulative precipitation over south central Sweden, given that at least one observation is extreme. The model belongs to the max-domain of attraction of popular Brown-Resnick max-stable processes (Brown and Resnick, 1977; Kabluchko et al., 2009) and retains the essential dependence structure of their corresponding ...

In a previous paper a novel Generalized Multiobjective Multitree model (GMM-model) was proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMM-model, in...

Evolutionary Algorithms (EAs) are deployed for multi-objective Pareto optimal design of Group Method of Data Handling (GMDH)-type neural networks that have been used for modelling of a complex process (such as explosive cutting process) using some input-output experimental data. In this way, EAs with a new encoding scheme is firstly presented to evolutionary design of the generalized GMDH-type ...

Though optimization problems in industrial electromagnetic design are often truly multiobjective, solving them by evolutionary Pareto Optimal Front approximation is often unpractical, due to the high computational cost of objective evaluations. In order to overcome this drawback, an extension of classical single-objective Generalized Response Surface (GRS) methods to Pareto-optimal front approx...

The problem of traffic accidents in Indonesia has a high level risk. In an effort to minimize losses due accidents, it is necessary study the data and characteristics identify these events as extreme events. This was conducted find out how estimate shape scale parameters using Maximum Likelihood Estimation (MLE), explore on accident Indonesia. method used analyze value Extreme Value Theory. One...