A competitive new multi-objective optimization genetic algorithm based on apparent front ranking

Evolutionary algorithms built on Pareto-dominance suffer from a loss of selection pressure as the number of objectives increases and the probability of finding non -dominated solutions in the population decreases. Furthermore, Pareto-dominance is computationally expensive because of the pairwise comparisons necessary to rank the individuals of a population. The paper introduces a new genetic algorithm for multi -objective optimization based on Apparent Front Ranking and crowding distance, called Controlled Apparent Front Zones Genetic Algorithm (CAFZGA). To avoid pairwise comparisons, CAFZGA first generates the main Apparent Front Boundary (AFB) with the help of a small set of support vectors and creates secondary AFBs as shifted versions of the main AFB. The space of objectives is divided into zones by the AFBs. The zones play a similar role in CAFZGA as the non -dominated fronts do in the Controlled Non -dominated Sorting Genetic Algorithm CNSGA-II. The zone ranking becomes the main criterion in differentiating individuals, with crowding distance being the tiebreak criterion. The method is shown to be extremely flexible because the AFBs are adjusted for each generation. Computationally, CAFZGA is more efficient than GAs using Pareto-dominance because the set of support vectors for generating the AFBs is significantly smaller than the population. CAFZGA is applied to the problem of optimizing the configuration of a Grid ALU Processor (GAP) and is shown to be competitive and sometimes even better than well -established GAs like CNSGA-II and Fuzzy -Dominance -Driven Genetic Algorithm FDD-GA.