论文标题
一种新型的多目标无速度布尔粒子群优化
A Novel Multi-Objective Velocity-Free Boolean Particle Swarm Optimization
论文作者
论文摘要
本文在文献中首次将布尔粒子群优化扩展到多目标设置。我们提出的新布尔算法MBONVPSO明显通过省略速度更新规则来简化，并且由于将“噪声”项纳入了位置更新规则，从而增强了探索能力，以防止粒子被困在本地Optima中。我们的算法还利用外部存档来存储非主导的解决方案并实现拥挤距离以鼓励解决方案多样性。在基准测试中，与基准替代方案相比，MBONVPSO为所有考虑的多目标测试功能而产生了高质量的帕累托阵线，并在高达600个离散维度的搜索空间中具有竞争性能。
This paper extends boolean particle swarm optimization to a multi-objective setting, to our knowledge for the first time in the literature. Our proposed new boolean algorithm, MBOnvPSO, is notably simplified by the omission of a velocity update rule and has enhanced exploration ability due to the inclusion of a 'noise' term in the position update rule that prevents particles being trapped in local optima. Our algorithm additionally makes use of an external archive to store non-dominated solutions and implements crowding distance to encourage solution diversity. In benchmark tests, MBOnvPSO produced high quality Pareto fronts, when compared to benchmarked alternatives, for all of the multi-objective test functions considered, with competitive performance in search spaces with up to 600 discrete dimensions.
