STATService: Herramienta de análisis estadístico como soporte para la investigación con Metaheurísticas

Actualmente, la aplicación de técnicas estadísticas es una necesidad cuando se trabaja con metaheurísticas. A pesar de que existen multitud de paquetes software para el cálculo estadístico, estos sistemas exigen en la mayoría de los casos la instalación de componentes y no son fáciles de integrar con otros sistemas. En este artículo presentamos un conjunto de herramientas para análisis estadístico denominado STATService, que puede usarse a través de un portal web. STATService provee además interfaces programáticas a través de servicios web que hacen que su integración con otros sistemas sea extremadamente simple, independientemente de la plataforma y tecnologías concretas de dichos sistemas

Initial Solution Heuristic for Portfolio Optimization of Electricity Markets Participation

Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Metaheuristics for online drive train efficiency optimization in electric vehicles

Utilization of electric vehicles provides a solution to several challenges in today’s individual mobility. However, ensuring maximum efficient operation of electric vehicles is required in order to overcome their greatest weakness: the limited range. Even though the overall efficiency is already high, incorporating DC/DC converter into the electric drivetrain improves the efficiency level further. This inclusion enables the dynamic optimization of the intermediate voltage level subject to the current driving demand (operating point) of the drivetrain. Moreover, the overall drivetrain efficiency depends on the setup of other drivetrain components’ electric parameters. Solving this complex problem for different drivetrain parameter setups subject to the current driving demand needs considerable computing time for conventional solvers and cannot be delivered in real-time. Therefore, basic metaheuristics are identified and applied in order to assure the optimization process during driving. In order to compare the performance of metaheuristics for this task, we adjust and compare the performance of different basic metaheuristics (i.e. Monte-Carlo, Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimization). The results are statistically analyzed and based on a developed simulation model of an electric drivetrain. By applying the bestperforming metaheuristic, the efficiency of the drivetrain could be improved by up to 30% compared to an electric vehicle without the DC/DC- converter. The difference between computing times vary between 30 minutes (for the Exhaustive Search Algorithm) to about 0.2 seconds (Particle Swarm) per operating point. It is shown, that the Particle Swarm Optimization as well as the Evolutionary Algorithm procedures are the best-performing methods on this optimization problem. All in all, the results support the idea that online efficiency optimization in electric vehicles is possible with regard to computing time and success probability

Initial Solution Heuristic for Portfolio Optimization of Electricity Markets Participation

Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries

Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution. However, more participation also leads to increased “noise,” which makes identifying the most likely solution from a broader pool of recommendations (i.e., diagnostic suggestions) difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. The primary objectives of this study are: (1) to investigate means to enhance the solution pool by increasing participation of solution providers referred to as “medical detectives” or “detectives,” and (2) to explore ways of selecting the most likely diagnosis from a set of alternative possibilities recommended by medical detectives. Our results suggest that our strategy of using multiple methods for evaluating recommendations by detectives leads to better predictions. Furthermore, cases with higher perceived quality and more negative emotional tones (e.g., sadness, fear, and anger) attract more detectives. Our findings have strong implications for research and practice

A new IPSO-SA approach for cardinality constrained portfolio optimization

The problem of portfolio optimization has always been a key concern for investors. This paper addresses a realistic portfolio optimization problem with floor, ceiling, and cardinality constraints. This problem is a mixed integer quadratic programming where traditional optimization methods fail to find the optimal solution, efficiently. The present paper develops a new hybrid approach based on an improved particle swarm optimization (PSO) and a modified simulated annealing (SA) methods to find the cardinality constrained efficient frontier. The proposed algorithm benefits simple and easy characteristics of PSO with an adaptation of inertia weights and constriction factor. In addition, incorporating an SA procedure into IPSO helps escaping from local optima and improves the precision of convergence. Computational results on benchmark problems with up to 225 assets signify that our proposed algorithm exceeds not only the standard PSO but also the other heuristic algorithms previously presented to solve the cardinality constrained portfolio problem

Routing Unmanned Aerial Vehicles While Considering General Restricted Operating Zones

U.S. military forces employ unmanned aerial vehicles (UAVs) to conduct intelligence-gathering missions worldwide. For a typical mission, commanders may task UAV operators to gather imagery on 100 or more sites or targets. UAV operators must quickly prepare mission plans that meet the needs of their commanders while dealing with real-world constraints such as time windows, site priorities, imagery requirements, UAVs with different capabilities (i.e. imagery equipment, speed, and range), and UAVs departing from different bases. Previous AFIT research provided the UAV Battlelab with a tool, AFIT Router, for generating high-quality routes to aid mission planning. This research enhances the AFIT Router by providing the ability to define general restricted operating zones and to build routes that consider these zones. This research also examines and compares a probabilistic tabu search heuristic and two reactive tabu search heuristics for solving vehicle routing problems

Improving the drug discovery process by using multiple classifier systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Machine learning methods have become an indispensable tool for utilizing large knowledge and data repositories in science and technology. In the context of the pharmaceutical domain, the amount of acquired knowledge about the design and synthesis of pharmaceutical agents and bioactive molecules (drugs) is enormous. The primary challenge for automatically discovering new drugs from molecular screening information is related to the high dimensionality of datasets, where a wide range of features is included for each candidate drug. Thus, the implementation of improved techniques to ensure an adequate manipulation and interpretation of data becomes mandatory. To mitigate this problem, our tool (called D2-MCS) can split homogeneously the dataset into several groups (the subset of features) and subsequently, determine the most suitable classifier for each group. Finally, the tool allows determining the biological activity of each molecule by a voting scheme. The application of the D2-MCS tool was tested on a standardized, high quality dataset gathered from ChEMBL and have shown outperformance of our tool when compare to well-known single classification models