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....

We present the Integrated Preference Functional (IPF) for comparing the quality of proposed sets of near-pareto-optimal solutions to bi-criteria optimization problems. Evaluating the quality of such solution sets is one of the key issues in developing and comparing heuristics for multiple objective combinatorial optimization problems. The IPF is a set functional that, given a weight density fun...

Many scientific fields, such as engineering, data analytics, and deep learning, focus on optimization. Optimization problems are classified into two types based the number of optimized objective functions: single multi optimization problems. In this paper, a comparative review since 2000 using one deterministic stochastic modelling approaches called goal programming (GP) genetic algorithm (GA) ...