公平约束多目标粒子群优化算法
Fairly Constricted Multi-Objective Particle Swarm Optimization
摘要：
在粒子群优化算法（PSO）中使用指数平均动量（EM）比普通PSO算法具有更大的优势。在单目标设置中，它导致更快的收敛和避免局部极小值。自然，人们会期望EM的相同优点延续到多目标设置。因此，我们扩展了最先进的多目标优化（MOO）求解器，SMPSO，通过将EM在其中。因此，我们开发的数学形式主义的收缩公平性，这是在扩展的SMPSO算法的核心。建议的求解器匹配的性能SMPSO在ZDT，DTLZ和WFG问题套件，甚至在某些情况下优于它。
英文摘要：
It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm. In the single-objective setting, it leads to faster convergence and avoidance of local minima. Naturally, one would expect that the same advantages of EM carry over to the multi-objective setting. Hence, we extend the state of the art Multi-objective optimization (MOO) solver, SMPSO, by incorporating EM in it. As a consequence, we develop the mathematical formalism of constriction fairness which is at the core of extended SMPSO algorithm. The proposed solver matches the performance of SMPSO across the ZDT, DTLZ and WFG problem suites and even outperforms it in certain instances.
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