Application of A Self-adaption Dual Population Genetic Algorithm in Multi-objective Optimization Problems
Abstract: Multi-objective
evolutionary algorithm is a powerful tool in resolving multi-objective
optimization problems. This algorithm inherits the advantages of parallel
random search, strong global searching capability and the ability to solve
highly-complicated non-linear problems of evolutionary algorithm and it is
usually used in the optimization problems with multiple mutual conflicts.
However, such algorithms are slow in convergence and easy to be trapped in
local optimal solution. This paper proposes a multi-objective dual population
genetic algorithm (MODPGA) and explores the improvement strategies of
multi-objective genetic algorithm. The adoption of self-adaption and dual
population strategy can guarantee that the algorithm of this paper can converge
to Pareto solution set in a reliable and quick manner and it can perform more
extensive search on the objective function space and conduct more samples on
multi-objective functions so as to be closer to the approximate optimal
solution set of global optimal solutions. This solution set also includes more
optimal feasible points and provides reliable basis for the decision making.
Author: Cheng Zhang, Hao Peng
Journal Code: jptkomputergg160214
