标题： 混沌嵌入对立学习的引力搜索算法 显示英文标题

标题： Chaos embedded opposition based learning for gravitational search algorithm

摘要： 由于其强大的搜索机制，引力搜索算法（GSA）在不同研究领域中获得了广泛的关注。 然而，停滞现象会降低其在刚性和复杂多模态问题中寻找全局最优解的能力。 本文提出了一种改进的GSA变体，该变体将嵌入混沌的反向学习方法引入基本GSA，以实现无停滞的搜索。 此外，引入了一种基于正弦余弦的混沌引力常数，以更有效地平衡探索与开发能力之间的权衡。 所提出的变体在23个经典基准问题、15个CEC 2015测试套件中的测试问题以及15个CEC 2014测试套件中的测试问题上进行了测试。 不同的图形分析和实证分析表明，所提出的算法在传统元启发式算法和大多数最近的GSA变体中具有优越性。 显示英文摘要

摘要： Due to its robust search mechanism, Gravitational search algorithm (GSA) has achieved lots of popularity from different research communities. However, stagnation reduces its searchability towards global optima for rigid and complex multi-modal problems. This paper proposes a GSA variant that incorporates chaos-embedded opposition-based learning into the basic GSA for the stagnation-free search. Additionally, a sine-cosine based chaotic gravitational constant is introduced to balance the trade-off between exploration and exploitation capabilities more effectively. The proposed variant is tested over 23 classical benchmark problems, 15 test problems of CEC 2015 test suite, and 15 test problems of CEC 2014 test suite. Different graphical, as well as empirical analyses, reveal the superiority of the proposed algorithm over conventional meta-heuristics and most recent GSA variants.