Multi-objective worst case optimization by means of evolutionary algorithms

Many real-world optimization problems are subject to uncertainty. A possible goal is then to find a solution which is robust in the sense that it has the best worst-case performance over all possible scenarios. However, if the problem also involves mul- tiple objectives, which scenario is “best” or “worst” depends on the user’s weighting of the different criteria, which is generally difficult to specify before alternatives are known. Evolutionary multi-objective optimization avoids this problem by searching for the whole front of Pareto optimal solutions. This paper extends the concept of Pareto dominance to worst case optimization problems and demonstrates how evolu- tionary algorithms can be used for worst case optimization in a multi-objective setting

Thermo-Elastic-Plastic Plate Bending By a Boundary Element Method with Initial Plastic Moments (Flame, Line Heating).

This work describes the analysis of thermo-elastic-plastic plate bending using a boundary integral equation formulation. In particular, the line heating method, which is used to bend plates in shipyards, is the motivation. The governing equations have been derived using Kirchoff's theory. It is shown that the bending problem is coupled with the in-plane problem for the thermo-elastic-plastic plate bending, in spite of the assumption of small deflections. The bending equation is transformed into integral equations which are solved using st and ard boundary element techniques. The plasticity, as well as the thermal and external lateral loads, appears in a domain integral. The solution is obtained by an incremental loading procedure with the initial incremental plastic moments calculated by an iterative method. Several study cases, including examples on line heating, are examined, and good agreement is shown with published results.Ph.D.MechanicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/160840/1/8600508.pd

Transferability of Multi-Objective Neuro-Fuzzy Motion Controllers: Towards Cautious and Courageous Motion Behaviors in Rugged Terrains

This study is motivated by the need to develop generic neuro-fuzzy motion controllers for autonomous vehicles that may traverse rugged terrains. Three types of target problems are investigated. These problems differ in terms of the expected motion behavior, including cautious, intermediate, and courageous behaviors. The target problems are defined as evolutionary multi-objective problems aiming to evolve near optimal neuro-fuzzy controllers that can operate in a variety of scenarios. To enhance the evolution, sequential transfer optimization is considered, where each of the source problems is defined and solved as a bi-objective problem. The performed experimental study demonstrates the ability of the proposed search approach to find neuro-fuzzy controllers that produce the required motion behaviors when operating in various environments with different motion difficulties. Moreover, the results of this study substantiate the hypothesis that solutions with performances near the edges of the obtained approximated bi-objective Pareto fronts of the source problems provide better transferability as compared with those that are associated with performances near the center of the obtained fronts

Deciphering the COVID-19 Health Economic Dilemma (HED): A Scoping Review

Lessons learnt from the initial stages of the COVID-19 outbreak indicate the need for a more coordinated economic and public health response. While social distancing has been shown to be effective as a non-pharmaceutical intervention (NPI) measure to mitigate the spread of COVID-19, the economic costs have been substantial. Insights combining epidemiological and economic data provide new theoretical predictions that can be used to better understand the health economy tradeoffs. This literature review aims to elucidate perspectives to assist policy implementation related to the management of the ongoing and impending outbreaks regarding the Health Economic Dilemma (HED). This review unveiled the need for information-based decision-support systems which will combine pandemic spread modelling and control, with economic models. It is expected that the current review will not only support policy makers but will also provide researchers on the development of related decision-support-systems with comprehensive information on the various aspects of the HED