IEEE/CAA Journal of Automatica Sinica
Citation:  L. Zhang, Q. Kang, Q. Deng, L. Y. Xu, and Q. D. Wu, “A line complexbased evolutionary algorithm for manyobjective optimization,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1150–1167, May 2023. doi: 10.1109/JAS.2023.123495 
In solving manyobjective optimization problems (MaOPs), existing nondominated sortingbased multiobjective evolutionary algorithms suffer from the fast loss of selection pressure. Most candidate solutions become nondominated during the evolutionary process, thus leading to the failure of producing offspring toward Paretooptimal front with diversity. Can we find a more effective way to select nondominated solutions and resolve this issue? To answer this critical question, this work proposes to evolve solutions through line complex rather than solution points in Euclidean space. First, Plücker coordinates are used to project solution points to line complex composed of position vectors and momentum ones. Besides position vectors of the solution points, momentum vectors are used to extend the comparability of nondominated solutions and enhance selection pressure. Then, a new distance function designed for highdimensional space is proposed to replace Euclidean distance as a more effective distancebased estimator. Based on them, a novel manyobjective evolutionary algorithm (MaOEA) is proposed by integrating a line complexbased environmental selection strategy into the NSGAIII framework. The proposed algorithm is compared with the state of the art on widely used benchmark problems with up to 15 objectives. Experimental results demonstrate its superior competitiveness in solving MaOPs.
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