Self organizing multi-objective optimization problem

Weighted-sum approach genetic algorithm (GA) is one of the popular methods applied to solve multi-objectives optimization problems because it is a straight forward formulation and computationally efficient. However, this approach has some limitations because of the difficulty in selecting an appropriate weight for each objective and the need of some knowledge about the problems. The weight selection is a subjective decision which is usually based on trial and errvr and is impractical for complei engineering problems. In order to overcome these problems, this paper proposes a new self organizing genetic algorithm (SOGA) for multi-objective optimization problems. The SOGA involves GA within GA evaluation process which optimally tunes the weight of each objective function and applies weighted-sum approach for fitness evaluation process. This algorithm has been tested for optimization of components placement on printed circuit boards. The results show that SOGA is able to obtain a better minimum value as compared to other methods such as fix weight GA, random weight GA and formulated weight based GA methods