An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients not suitable for these problems. Meta-heuristic popularly employed to deal because they can promisingly jump out of optima and do need any gradient information. The arithmetic algorithm (AOA), a recently developed meta-heuristic algorithm, uses operators (multiplication, division, subtraction, addition) solve including ones. However, the exploration exploitation AOA effective handle some complex this paper, an opposition learning spiral modelling based AOA, namely OSAOA, is proposed enhancing performance. It improves from two perspectives. first perspective, opposition-based (OBL) committed taking both candidate solutions their opposite into consideration improving global search high probability jumping minima. Then, introduced as second which particularly useful in getting gathering faster accelerating convergence speed later stage. addition, OSAOA compared other existing advanced on 23 benchmark functions four problems: three-bar truss design, cantilever beam pressure vessel tubular column design. From our simulations provide better results dealing