This article presents a limit-cycle-based mutant multiobjective pigeon-inspired optimization (PIO). In this algorithm, the limit-cycle-based mechanism is devised to consider the factors that affect the flight of pigeons to simplify the multiobjective PIO algorithm. The mutant mechanism is incorporated to strengthen the exploration capability in the evolutionary process. Additionally, the application of the dual repository makes the nondominated solutions stored and selected to guide the flight of pigeons. Attributed to the limit-cycle-based mutant mechanisms, this algorithm not only obtains the faster convergence speed and higher accuracy but also improves its population diversity. To confirm the universal application of this algorithm, theoretical analysis of the convergence is discussed in this article. Finally, comparative experiments of our proposed algorithm and other five multiobjective methods are conducted to verify the accuracy, efficiency, and convergence stability of the proposed algorithm.

中文翻译：

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基于极限环的突变体多目标鸽子优化


本文提出了一种基于极限环的突变多目标鸽子优化（PIO）。该算法设计了基于极限环的机制来考虑影响鸽子飞行的因素，以简化多目标PIO算法。引入突变机制，增强进化过程中的探索能力。此外，双存储库的应用使得非支配解被存储和选择以引导鸽子的飞行。得益于基于极限环的突变机制，该算法不仅获得了更快的收敛速度和更高的精度，而且提高了种群多样性。为了证实该算法的普遍适用性，本文对收敛性进行了理论分析。最后，对所提出的算法与其他五种多目标方法进行了对比实验，验证了所提出算法的准确性、效率和收敛稳定性。