Extension for multi-objective genetic algorithms based on the dynamic population size model

Abstract

The paper describes an original way to improve the efficiency of genetic algorithms for multi-objective optimization using the proposed model for dynamic control of population size. This model is used as the extension (modification) for any evolutionary methods of multi-objective optimization. It is based on using the dynamic population size allows multi-objective genetic algorithms to adapt for search space, to enhance the diversity of population and to increase the number of non-dominated solutions. The model uses two additional parameters – age and lifetime of an individual. The value of the first parameter is equal to the number of generations the individual stays in the population. The age increases after each generation, and if it has exceeded lifetime parameter value, an individual is removed from a population. The special expressions for the lifetime parameter calculation were obtained. An individual’s lifetime depends on its Pareto rank and fitness value. A lifetime increases if ones have improved in comparison with a previous generation. The efficiency of dynamic population size using was studied at solving the test multi-objective optimization problems set with a different complexity. In many cases the algorithms used dynamic population size achieved a better distribution and convergence to the true Pareto-front in comparison with other tested genetic algorithms.