  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.
