Multi‑Strategy Equilibrium Optimizer: An Improved Meta-Heuristic Tested On Numerical Optimization And Engineering Problems

In order to solve complex numerical optimization problems and engineering problems more effectively, an Improved Equilibrium Optimizer (IEO) based on multi-strategy optimization is proposed. Firstly, Tent mapping is used to initialize the algorithm instead of randomly generating initial population in basic Equilibrium Optimizer. The diversified initial population lays a good foundation for global search of the algorithm. Moreover, nonlinear time parameter is used to update the position equation, which dynamically balances the exploration and exploitation phases of IEO. Finally, Lens Opposition‑based Learning (LOBL) is introduced, which can avoid local optimization by improving the population diversity of the algorithm. Simulation experiments are carried out on 23 classical functions, IEEE CEC2017 problems and IEEE CEC2019 problems, and the stability of the algorithm is further analyzed by Friedman statistical test and box plots. Experimental results show that IEO has good solution accuracy and robustness. In addition, six engineering design problems are solved, and the results show that IEO not only has high optimization efficiency, but also can achieve cost minimization.