High equilibrium optimizer for global optimization

With the advent of the information age, people have higher requirements for basic algorithms. Meta-heuristic algorithms have received wide attention as a high-level strategy to study and generate fully optimized solutions to data-driven optimization problems. Using the advantage of equilibrium optimizer (EO) with better balance mode, combined with the strategy of memetic algorithm, different proportion of temperature is introduced in different stages. That is, EO and thermal exchange optimization (TEO) are fused to obtain a new highly balanced optimizer (HEO). While keeping the guiding strategy and memory mode unchanged of EO, the accuracy of optimization is greatly improved. 14 well-known benchmark functions and 7 selective algorithms were used for HEO evaluation comparison experiments. On the basis of the fitness function curve, the optimal solution and other experimental data are tested statistically. The experimental results show that the improved algorithm has high accuracy and stability, but at the cost of running a little more time. Application testing of complex engineering problems is also one of the main purposes of algorithm design. In this paper, three typical engineering design problems (three truss, welded beam and rolling bearing design) are tested and the experimental results show that this algorithm has certain competitiveness and superiority in classical engineering design.

Automated summary

By fusing equilibrium optimizer (EO) with thermal exchange optimization (TEO) to create a new highly balanced optimizer (HEO), this improved algorithm significantly enhances optimization accuracy. Experimental results demonstrate that, while requiring slightly more time, the algorithm exhibits high accuracy and stability, showing competitiveness and superiority in classical engineering design problems.