Artificial Bee Colony induced multi-objective optimization in presence of noise

The paper aims at designing new strategies to extend traditional Non-dominated Sorting Bee Colony algorithm to proficiently obtain Pareto-optimal solutions in presence of noise on the fitness landscapes. The first strategy, referred to as adaptive selection of sample-size, is employed to balance the trade-off between accurate fitness estimate and computational complexity. The second strategy is concerned with determining statistical expectation, instead of conventional averaging of fitness-samples as the measure of fitness of the trial solutions. The third strategy attempts to extend Goldberg´s approach to examine possible placement of a slightly inferior solution in the optimal Pareto front using a more statistically viable comparator. Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to four performance metrics, when examined on a test-suite of 23 standard benchmarks with additive noise of three statistical distributions.