基于定期竞争学习的多目标粒子群优化算法

为提高种群的多样性和算法的收敛性，提出一种基于定期竞争学习机制的多目标粒子群算法。该算法将多目标粒子群算法和竞争学习机制相结合，即每隔一定迭代代数便使用一次竞争学习机制，很好地保持了种群的多样性；同时，该算法不需要全局最优粒子的外部存档，而是从当前代种群中选取一部分优秀的粒子，再从这些优秀的粒子中随机选取一个作为全局最优粒子，能够有效提升算法的收敛性。将提出的算法与基于分解的多目标粒子群算法（MPSOD）、基于竞争机制且快速收敛的多目标粒子群（CMOPSO）算法、参考向量引导的多目标进化算法（RVEA）等8个算法在21个标准测试函数上进行了比较，结果表明，所提算法的帕累托（Pareto）前沿更加均匀，在世代距离（IGD）上会更加小。

Scheduled competition learning based multi-objective particle swarm optimization algorithm

In order to improve the diversity of population and the convergence performance of algorithm, a Scheduled competition learning based Multi-Objective Particle Swarm Optimization (SMOPSO) algorithm was proposed. The multi-objective particle swarm optimization algorithm and the competition learning mechanism were combined and the competition learning mechanism was used in every certain iterations to maintain the diversity of the population. Meanwhile, to improve the convergence of algorithm without using the global best external archive, the elite particles were selected from the current swarm, and then a global best particle was randomly selected from these elite particles. The performance of the proposed algorithm was verified on 21 benchmarks and compared with 8 algorithms, such as Multi-objective Particle Swarm Optimization algorithm based on Decomposition (MPSOD), Competitive Mechanism based multi-Objective Particle Swarm Optimizer (CMOPSO) and Reference Vector guided Evolutionary Algorithm (RVEA). The experimental results prove that the proposed algorithm can get a more uniform Pareto front and a smaller Inverted Generational Distance (IGD).