Dynamic Multiobjective Evolutionary Algorithm With Two Stages Evolution Operation

Abstract

Multiobjective optimization problems occur in many situations and aspects of the engineering optimization field. In reality, many of the multiobjective optimization problems are dynamic in nature, i.e. their Pareto fronts change with the time or environment parameter; these optimization problems most often are called dynamic multiobjective optimization problem (DMOP). The major problems in solving DMOP are how to track and predict the Pareto optimization solutions and how to get the uniformly distributed Pareto fronts, which change with the time parameter. In this paper, a new dynamic multi-objective optimization evolutionary algorithm with two stages evolution operation is proposed for solving the kind of dynamic multiobjective optimization problem in which the Pareto optimal solutions change with time parameter continuously and slowly. At the first stage, when the time parameter has been changed, we use a new core distribution estimation algorithm to generate the new evolution population in the next environment; at the second stage, when the environment of the optimization problem keeps unchanged, a new crossover operator and a mutation operator are used to search the Pareto optimal solutions in current environment. Moreover, three performance metric methods for DMOP based on the generation distance, the spacing and the error ratio are also given. The computer simulations are made on three dynamic multi-objective optimization problems, and the results indicate the proposed algorithm is effective for solving DMOP.

Keywords::

• Dynamic multiobjective optimization
• Evolutionary algorithm
• Two stages evolution
• Pareto optimal solution

Additional information

Notes on contributors

Chun-an Liu

Chun-an Liu received B.S. degree in Science of Information and Computation from Baoji University of Arts and Sciences of China in 1997, and his M.S. degree and Ph.D. degree in Applied Mathematics from Xidian University, China, in 2005 and 2008. Currently, he works in Baoji University of Arts and Sciences, China. His research interests include engineering optimization, evolutionary computation, and air traffic flow management. Email: [email protected].

Huamin Jia

Dr Huamin Jia is the Senior Lecturer in Avionics Engineering at Cranfield University. He received his MSc (Eng.) in Computer Software Engineering from University of Science and Technology of China, Hefei, China in 1988, and his PhD in Avionics Systems from College of Aeronautics, Cranfield University, UK in 2004. Dr Jia's current research interests cover data fusion methodologies for integrated navigation guidance control and traffic/obstacle conflict detection and avoidance, performance-based navigation and 4D trajectory optimisation, air traffic flow optimisation, and avionics system safety assessment.