Chaos embedded opposition based learning for gravitational search algorithm

TL;DR

This paper introduces a novel GSA variant that embeds chaos and opposition-based learning, along with a sine-cosine chaotic gravitational constant, to enhance global search capabilities and prevent stagnation in complex optimization problems.

Contribution

It proposes a new GSA variant integrating chaos-embedded opposition-based learning and a sine-cosine chaotic gravitational constant for improved global search performance.

Findings

Outperforms conventional meta-heuristics on benchmark problems.

Shows superior results on CEC 2015 and CEC 2014 test suites.

Demonstrates effective balance between exploration and exploitation.

Abstract

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.