A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization
While Pareto-based multi-objective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective problems, due to the increasing proportion of nondominated solutions and the lack of sufficient selection pressure, has also been gradually recognized. In this paper, we revive an early-developed and computationally expensive strength Pareto based evolutionary algorithm by introducing an efficient reference direction based density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multi- and many-objective problems. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed method shows very competitive performance on both multi- and many-objective problems considered in this study. Besides, our extensive investigations and discussions reveal an interesting finding, that is, diversity-first-and-convergence-second selection strategies may have great potential to deal with many-objective optimization.
Citation : Jiang, S. and Yang, S. (2016) A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary Computation, 21 (3), pp. 329-346
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes