An Enhanced Annealing Genetic Algorithm for Multi-Objective Optimization Problems

In this paper, we present a new algorithm | an Enhanced Annealing Genetic Algorithm for Multi-Objective Optimization problems (MOPs). The algorithm tackles the MOPs by a new quantitative measurement of the Pareto front coverage quality | Coverage Quotient. We then correspondingly design an energy function, a tness function and a hybridization framework, and manage to achieve both satisfactory results and guaran-teed convergence.