a multi objective honey bee mating optimization (hbmo) designed by online learning mechanism is proposed in this paper to optimize the double fuzzy-lead-lag (fll) stabilizer parameters in order to improve low-frequency oscillations in a multi machine power system. the proposed double fll stabilizer consists of a low pass filter and two fuzzy logic controllers whose parameters can be set by the ...

an integrated model considers all parameters and elements of different deficiencies in one problem. this paper presents a new integrated model of a supply chain that simultaneously considers facility location, vehicle routing and inventory control problems as well as their interactions in one problem, called location-routing-inventory (lri) problem. this model also considers stochastic demands ...

This paper presents the application of modified form of Particle Swarm Optimization as an optimization technique to the reconfiguration of electric distribution systems. The intended reconfiguration is an optimization and decision-making process which considers the maximization of the number of loads supplied associated to the minimization of the number of closed switches. A novel selection reg...

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on the single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g. Ranking Loss) or a heuristic function. The basic assumption is that the optimization over one single objective can i...

Recent generations of FPGAs allow run-time partial reconfiguration. To increase the efficacy of reconfigurable computing, multitasking on FPGAs is proposed. One of the challenging problems in multitasking systems is online template placement. In this paper, we describe how existing algorithms work, and propose a new multi-stage method for mapping of tasks to reconfigurable hardware. Also a new ...

In general, Multi-objective Evolutionary Algorithms do not guarantee find solutions in the Pareto-optimal set. We propose a new approach for solving decomposable deceptive multi-objective problems that can find all solutions of the Pareto-optimal set. Basically, the proposed approach starts by decomposing the problem into subproblems and, then, combining the found solutions. The resultant appro...

This dissertation presents multi-objective multi-task learning, a new learning framework. Given a fixed sequence of tasks, the learned hypothesis space must minimize multiple objectives. Since these objectives are often in conflict, we cannot find a single best solution, so we analyze a set of solutions. We first propose and analyze a new learning principle, empirically efficient learning. From...

Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it au...