Kim: A new structure for optimization problems

The multi-objective optimization algorithms are used as the best optimizer in many design issues. One of the main challenge for these algorithms is that increasing the number of objective functions leads poor performing of the algorithm. Reducing the selection pressure is the main reason of this phenomenon. In order to overcome this problem, the population diversity should be controlled. In this regard, this study developed a new evolutionary algorithm through resolving this dilemma. In the proposed method, a measure is considered for estimating the diversity of individuals in the population to adaptively control the rate of population diversity. A new fitness evaluation is also provided in this paper for assessing the fitness of chromosomes. So in this schema the selection of chromosomes is based on their contribution to population diversity in addition to being based on their fitness. The obtained results proved that the performance of the proposed algorithm has been improved through various tests. It is worth noting that the potential of the proposed method is examined on the most recent test function that presented in this field.