During the past few decades, multi-agent optimization problems have drawn increased attention from research community. When multiple objective functions are present among agents, many works optimize sum of these functions. However, this formulation implies a decision regarding relative importance each objective: optimizing is special case multi-objective problem in which all objectives prioriti...

Federated learning has emerged as a promising, massively distributed way to train joint deep model over large amounts of edgedevices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated multi-objective optimization propose new algorithm <monospace xmlns:mml="http://www....

The practical application of a multi-objective optimization strategy based on evolutionary algorithms was proposed to optimize the plastics thermoforming process. For that purpose, in this work, differently from other works literature, shaping step considered individually with aim optimizing thickness distribution final part originated sheets characterized by different profiles, such as constan...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...

this paper presents a clear monograph on the optimization of thermal instability resistance of the fg (functionally graded) flat structures. for this aim, two fg flat structures, namely an fg beam and an fg circular plate, are considered. these structures are assumed to obey the first-order shear deformation theory, three-parameters power-law distribution of the constituents, and clamped bounda...