Type-2 Fuzzy Induced Non-dominated Sorting Bee Colony for Noisy Optimization

A novel multi-objective optimization algorithm is introduced in the paper to proficiently obtain Pareto-optimal solutions in the noisy fitness landscapes. First, a non-linear functional relationship between the fitness variance in the local neighborhood of a trial solution and the sample size for its periodic fitness evaluation is proposed. The second strategy is concerned with determining defuzzified centroidal value of the noisy fitness samples, instead of their conventional averaging, as the effective fitness measure of the trial solutions. Finally, to ensure the diversity of quality solutions in the noisy fitness landscapes, a new selection criterion induced by the crowding distance measure and the distribution pattern of noisy fitness samples is formulated. Experiments undertaken to validate the performance of the extended algorithm affirm its superiority to its contenders with respect to hyper volume ratio, when examined on a test suite of 23 standard benchmarks contaminated with additive noise of five statistical distributions.