Jie Zhang

TL;DR: This work proposes a coevolutionary particle swarm optimization with a bottleneck objective learning (BOL) strategy for many-objective optimization, and develops a solution reproduction procedure with both an elitist learning strategy and a juncture learning strategy to improve the quality of archived solutions.

TL;DR: The comparison results show that DGLDPSO is better than or at least comparable to other state-of-the-art large-scale optimization algorithms and workflow scheduling algorithms.

TL;DR: An LBP-based adaptive DE (LBPADE) algorithm that enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP is proposed.

TL;DR: A novel estimation method is proposed to evaluate the contribution of variables using the historical information of the best overall fitness, and a dynamic grouping strategy is conducted to construct the dynamic subcomponent that evolves in the next evolutionary period.

TL;DR: The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging and an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA).