Abstract: Till now, several novel metaheuristic algorithms are proposed for global search. But only specific algorithms have become popular or attracted researchers, who are efficient in solving global optimization problems as well as real-world application problems. The Social Group Optimization (SGO) algorithm is a new metaheuristic bioinspired algorithm inspired by human social behavior that attracted researchers due to its simplicity and problem-solving capability. In this study, to deal with the problems of low accuracy and local convergence in SGO, the chaos theory is introduced into the evolutionary process of SGO. Since chaotic mapping has certainty, ergodicity, and stochastic property, by replacing the constant value of the self-introspection parameter with chaotic maps, the proposed chaotic social group optimization algorithm increases its convergence rate and resulting precision. The proposal chaotic SGO is validated through 13 benchmark functions and after that 9 structural engineering design problems have been solved. The simulated results have been noticed as competent with that of state-of-art algorithms regarding convergence quality and accuracy, which certifies that improved SGO with chaos is valid and feasible.

Key words: Chaos, Bionic algorithm, Constrained optimization, SGO, Design problem