A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing

TL;DR

This paper introduces Yi optimization (YI), a novel non-population-based meta-heuristic inspired by Yi Jing philosophy, demonstrating competitive performance and low time complexity in benchmark tests.

Contribution

It presents a new Yi-based optimizer that replaces Yin-Yang pairs with Yi-points and uses Levy flights, advancing non-population meta-heuristic methods.

Findings

YI shows highly competitive performance on IEEE CEC 2017 benchmarks.

YI maintains low time complexity compared to other optimizers.

YI outperforms classical and dynamical Yin-Yang optimizers in tests.

Abstract

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.