Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

There exist many multiobjective optimization problems (MOPs) containing a large number of decision variables in real-world applications, which are known as large-scale MOPs. Due to the ineffectiveness of existing operators in finding optimal solutions in a huge decision space, some decision variable division-based algorithms have been tailored for improving the search efficiency in solving large-scale MOPs. However, these algorithms will encounter difficulties when solving problems with complicated landscapes, as the decision variable division is likely to be inaccurate and time consuming. In this paper, we propose a competitive swarm optimizer (CSO)-based efficient search for solving large-scale MOPs. The…

• NN
  National Natural Science Foundation of China

  Awards: 61876123, U1804262, 61822301, 61672033
• NS
  Natural Science Foundation of Anhui Province

  Awards: 1808085J06, 1908085QF271
• SK
  State Key Laboratory of Synthetical Automation for Process Industries

  Award: PAL-N201805