In this paper a comparison of the most recent algorithms for Multiobjective Optimization is realized. For this comparison are used the followings algorithms: Strength Pareto Evolutionary Algorithm (SPEA), Pareto Archived Evolution Strategy (PAES), Nondominated Sorting Genetic Algorithm (NSGA II), Adaptive Pareto Algorithm (APA). The comparison is made by using five test functions.

We model high school students’ competition for college admissions as an all-pay contest with many players and prizes, and investigate how reducing the information revealed to colleges about students’ performance can improve students’ welfare in a Pareto sense. Less information reduces the assortativity of the resulting matching, which reduces welfare, but also mitigates competition and reduces ...

This paper investigates fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme (FPGA) for searching Pareto-optimal set, which is based on a new approach of fast fitness identification algorithm for individual and a clustering on the basis of external population updating scheme to maintain population diversity and even distribution of Pareto so...

Many real-world optimization problems involve balancing multiple objectives. When there is no solution that is best with respect to all objectives, it is often desirable to compute the Pareto front. This paper proposes queued Pareto local search (QPLS), which improves on existing Pareto local search (PLS) methods by maintaining a queue of improvements preventing premature exclusion of dominated...

Standard lessons from economics tell us that an externality creates inefficiency, and that this inefficiency can be removed by internalizing the externality. This papers considers how successfully these lessons can be extended to intergenerational externalities such as emissions of greenhouse gas. For intergenerational externalities, the standard lessons involve comparisons between states whose...

This paper presents an approach to the generalization of grayscale morphology to color images. Attaining such a generalization is strongly related to the issues of multivariate ranking and to the Pareto sets of multiobjective optimization. Some ranking schemes for multivariate data are recalled. For color morphology, the most important underlying ranking scheme is reduced ordering (also referre...

Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all candidates for which no other candidate scores better under both objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. We define a general Pareto produ...

The multi-objective multi-armed bandit (MOMAB) problem is a sequential decision process with stochastic rewards. Each arm generates a vector of rewards instead of a single scalar reward. Moreover, these multiple rewards might be conflicting. The MOMAB-problem has a set of Pareto optimal arms and an agent’s goal is not only to find that set but also to play evenly or fairly the arms in that set....

The Generalized Pareto Distribution is a very useful tool for modeling in many areas of economics, finance and insurance. The Generalized Pareto Distribution is commonly used for extreme value problems. Especially, the values which exceed the finite threshold, is the focus in extreme value problems like in insurance sector. The Generalized Pareto Distribution is well approach for modeling the s...