A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing
This work presents an incremental improvement in meta-heuristic optimizers for optimization tasks, potentially benefiting researchers in computational optimization.
The authors tackled the problem of single-objective optimization by proposing the Yi optimization (YI) algorithm, a non-population-based meta-heuristic inspired by Yi Jing philosophy, which achieved highly competitive performance on the IEEE CEC 2017 benchmark while maintaining low time complexity.
Drawing inspiration from the philosophy of Yi Jing, Yin-Yang pair optimization (YYPO) has been shown to achieve competitive performance in single objective optimizations. Besides, it has the advantage of low time complexity when comparing to other population-based optimization. As a conceptual extension of YYPO, we proposed the novel Yi optimization (YI) algorithm as one of the best non-population-based optimizer. Incorporating both the harmony and reversal concept of Yi Jing, we replace the Yin-Yang pair with a Yi-point, in which we utilize the Levy flight to update the solution and balance both the effort of the exploration and the exploitation in the optimization process. As a conceptual prototype, we examine YI with IEEE CEC 2017 benchmark and compare its performance with a Levy flight-based optimizer CV1.0, the state-of-the-art dynamical Yin-Yang pair optimization in YYPO family and a few classical optimizers. According to the experimental results, YI shows highly competitive performance while keeping the low time complexity. Hence, the results of this work have implications for enhancing meta-heuristic optimizer using the philosophy of Yi Jing, which deserves research attention.