Chaos embedded opposition based learning for gravitational search algorithm

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