This paper proposes the multi-objective variant of recently-introduced fitness dependent optimizer (FDO). The algorithm is called a (MOFDO) and equipped with all five types knowledge (situational, normative, topographical, domain, historical knowledge) as in FDO. MOFDO tested on two standard benchmarks for performance-proof purpose: classical ZDT test functions, which widespread suite that take...

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....