Abstract-While Pareto-based multiobjective 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 directionbased density estimator, a new fitness assignment scheme, and a new environmental selection strategy, for handling both multiobjective 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 multiobjective and many-objective problems considered in this paper. 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. Index Terms-Computational complexity, many-objective optimization, multiobjective optimization, reference direction, strength Pareto evolutionary algorithm (SPEA).
Jiang and Yang REF improved a early proposed strength Pareto-based EA by developing three techniques, i.e., a novel reference direction-based density estimator, a brand-new fitness function and a new environmental selection strategy.
A Strength Pareto Evolutionary Algorithm Based on Reference Direction for Multiobjective and Many-Objective Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Mathematics Computer Science