Multi-Objective Evolutionary Algorithm Based on the Fuzzy Similarity Measure
Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the fuzzy similarity measure between every individual and the referenced vector is solved and the fuzzy similarity measure is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.
J. F. Li et al., "Multi-Objective Evolutionary Algorithm Based on the Fuzzy Similarity Measure", Key Engineering Materials, Vols. 439-440, pp. 225-230, 2010