In this study, a hybrid approach combining trust region (TR) algorithm and particle swarm optimization (PSO) is proposed to solve multi-objective optimization problems (MOOPs). The proposed approach integrates the merits of both TR and PSO. Firstly, the MOOP converting by weighted method to a single objective optimization problem (SOOP) and some of the points in the search space are generated. ...

A major goal of smart grid technology (e.g., smart meters) is to provide consumers with demand response signals such as electricity tariff and CO2 footprint so that the consumers can consciously control their electricity consumption patterns. These demand response signals provide incentives for the consumers to help reduce peak energy demand by load balancing, as this is particularly relevant i...

This work presents a genetic algorithm (GA)-based optimization technique, called GA-ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing th...

This paper addresses the problem of capturing nondominated points on convex Pareto frontiers, which are encountered in invex multi-objective programming problems. An algorithm to find a piecewise linear approximation of the nondominated set of convex Pareto frontier are applied. Index Term-Approximation, Nondominated points, Invex multi-objective problems, Block norms.

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 ...

Multicriteria decision support methods are common in engineering design. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. It has long been known that a weighted sum, when used for multicriteria optimization, may fail to locate all points on a nonconvex Pareto frontier. More recent results from the optimization literature rel...

The assignment of multiobjective human resources is a very important phase of the decisionmaking process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise / fuzzy owing to incomplete or unobtainable information, which can be represented as ...

Given a multi-armed bandit problem it may be desirable to achieve a smallerthan-usual worst-case regret for some special actions. I show that the price for such unbalanced worst-case regret guarantees is rather high. Specifically, if an algorithm enjoys a worst-case regret of B with respect to some action, then there must exist another action for which the worst-case regret is at least Ω(nK/B),...

There has been a surge of research interest in developing tools and analysis for Bayesian optimization, the task of finding the global maximizer of an unknown, expensive function through sequential evaluation using Bayesian decision theory. However, many interesting problems involve optimizing multiple, expensive to evaluate objectives simultaneously, and relatively little research has addresse...

1. Abstract Multiobjective genetic algorithms (MOGAs) have successfully been used on a wide range of real world problems. However, it is generally accepted that the performance of most state-of-the-art multiobjective genetic algorithms tend to perform poorly for problems with more than four objectives, termed many-objective problems. The contribution of this paper is a new approach for identify...