An Efficient Generalized Multiobjective Evolutionary Algorithm


In this paper, we propose an efficient generalized multiobjective evolutionary algorithm(GMOEA) for finding all nondominated solutions of a multiobjective optimization problem (MOOP). In the proposed algorithm, a novel generalized Pareto-based scale-independent (GPSI) fitness value with no use of a weighted-sum of multiple objectives is applied for coping with the difficulty involved in making the trade-off decisions to arrive at a single measure of performance. Furthermore, orthogonal arrays are used to achieve intelligent crossover (IC) that the chromosomes of the children are formed from the best combinations of the better genes representing variables of a function from the parents rather than the random combinations of parents' genes. The proposed IC operation with the GPSI fitness value can economically find all nondominated solutions of MOOPs without additional use of traditional local search procedures. The simple and efficient general-purpose algorithm GMOEA is more superior for solving multiobjective optimization problems with a large number of parameters in convergence speed and accuracy. High performance of our algorithm is demonstrated by applying it to test functions gleaned from the literature It is shown empirically that GMOEA outperforms the existing methods.