Chaos embedded opposition based learning for gravitational search algorithm
This is an incremental improvement for researchers and practitioners in optimization algorithms, addressing stagnation in GSA for multi-modal problems.
The paper tackled stagnation in the Gravitational Search Algorithm (GSA) for complex optimization problems by proposing a variant that incorporates chaos-embedded opposition-based learning and a sine-cosine based chaotic gravitational constant, resulting in superior performance over conventional meta-heuristics and recent GSA variants as tested on 53 benchmark problems.
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.