Solving nonlinear engineering problems using UFIA: A social inspired metaheuristic algorithm

This paper proposes a novel socially inspired metaheuristic optimization technique called the undivided family interaction algorithm (UFIA). This technique is inspired by the social learning behaviour of humans through interactions between the people who are living together in an undivided family (multi-generational families). Hence, the main motivation behind the proposed UFIA technique is the learning behaviour of the children by mimicking the behaviour of the parents, uncles, and grandparents through observation and interaction which emulates this hierarchical interaction to achieve shared goals. The proposed UFIA technique is evaluated against well-established algorithms using 119 benchmark test functions including CEC-2017, CEC-2019, and CEC-2022 sets. The proposed algorithm has subsequently validated its applicability by applying it to solve ten-constrained real-life industrial engineering, two power system domain, and one path planning problems. The results of the proposed UFIA algorithm have been evaluated both quantitatively and qualitatively by comparing it with the results obtained by other techniques. The experimentation-based outcomes reveal that the proposed UFIA algorithm provides promising outcomes in terms of diversification, intensification, avoiding of local optima, and convergence rate. From the experimental results and non-parametric statistical tests confirm that UFIA outperforms the existing techniques and establishing its competitiveness in solving the complex optimization challenges.