A self?organizing weighted optimization based framework for large?scale multi?objective optimization

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, paper proposes self-organizing weighted based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original there are two main improvements our work. Firstly, S-WOF simplifies stage into one stage, which evaluating numbers and normal approaches adaptively adjusted on current state. Specifically, regarding number (i.e., t1), it is larger when population exploitation state, aims accelerate convergence speed, while t1 diminishing switching exploration more attentions put diversity maintenance. On other hand, t2), shows an opposite trend t1, small during but gradually increases later. In way, dynamic trade-off between achieved S-WOF. Secondly, further improve search ability decision space, efficient competitive swarm optimizer (CSO) implemented efficiency Finally, experimental results have validated superiority over several state-of-the-art algorithms.