An Adaptive Quantum-based Multiobjective Evolutionary Algorithm for Efficient Task Assignment in Distributed Systems

Multi-criterion quantum programming is a new paradigm of decision making for complex systems. Quantum-based multiobjective algorithm utilizes a new representation, called a Q-bit, for the probabilistic representation that is based on the concept of qubits. Evolutionary computing with Q-bit representation has a better characteristic of population diversity than other representations, since it can represent linear superposition of states probabilistically. We consider the multi-criterion problem of task assignment, where both a workload of a bottleneck computer and the cost of system are minimized; in contrast, a reliability of the distributed system is maximized.