An adaptive quantum-based evolutionary algorithm for multiobjective optimization

Authors:
Jerzy Balicki
Affiliations:
Naval University of Gdynia, Gdynia, Poland
Venue:
WSEAS Transactions on Systems and Control
Year:
2009

Citing 9
Cited 0

Grid resource management: state of the art and future trends

Grid resource management: state of the art and future trends
Algorithms for quantum computation: discrete logarithms and factoring

SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Numerical experiments on Pareto-optimal task assignment representations by Tabu-based evolutionary algorithm

WSEAS Transactions on Information Science and Applications
Some numerical experiments on multi-criterion tabu programming for finding Pareto-optimal solutions

WSEAS TRANSACTIONS on SYSTEMS
An adaptive quantum-based multiobjective evolutionary algorithm for efficient task assignment in distributed systems

ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Quantum-inspired evolutionary algorithm-based face verification

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Immune systems in multi-criterion evolutionary algorithm for task assignments in distributed computer system

AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

An Adaptive Quantum-based Multi-criterion Evolutionary Algorithm called AQMEA is a new paradigm of decision making for complex systems. Quantum-based algorithms utilize a new representation for the smallest unit of information, called a Q-bit, for the probabilistic representation that is based on the concept of qubits. Evolutionary computing with Q-bit chromosomes has a better characteristic of population diversity than other representations, since it can represent linear superposition of states probabilistically. Moreover, we consider the three-criterion problem of task assignment.