Abstract
Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm optimization will sometimes have particles that become trapped inside local minima, preventing them from identifying a global optimal solution. As a solution to this issue, this paper proposes a science-fiction inspired enhancement of particle swarm optimization where an impactful iteration is identified and the algorithm is rerun from this point, with a change made to the swarm. The proposed technique is tested using multiple variations on several different functions representing optimization problems and several standard test functions used to test various particle swarm optimization techniques.
Subject
Industrial and Manufacturing Engineering
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献