A Novel Enhanced Arithmetic Optimization Algorithm for Global Optimization

Abstract

The arithmetic optimization algorithm (AOA) is based on the distribution character of the dominant arithmetic operators and imitates addition (A), subtraction (S), multiplication (M) and division (D) to find the global optimal solution in the entire search space. However, the basic AOA has some drawbacks of premature convergence, easily falls into a local optimal value, slow convergence rate, and low calculation precision. To improve the overall optimization ability and overcome the drawbacks of the basic AOA, an enhanced AOA (EAOA) based on the Lé vy variation and the differential sorting variation is proposed to solve the function optimization and the project optimization. The Lé vy variation increases population diversity, broadens the optimization space, enhances the global search ability and improves the calculation precision. The differential sorting variation filters out the optimal search agent, avoids search stagnation, enhances the local search ability and accelerates the convergence rate. The EAOA realizes complementary advantages of the Lé vy variation and the differential sorting variation to avoid falling into the local optimum and the premature convergence. The sixteen benchmark functions and five engineering design projects are applied to verify the effectiveness and feasibility of the EAOA. The EAOA is compared with other algorithms by minimizing the fitness value, such as artificial bee colony, ant line optimizer, cuckoo search, dragonfly algorithm, moth-flame optimization, sine cosine algorithm, water wave optimization and arithmetic optimization algorithm. The experimental results show that the overall optimization ability of the EAOA is superior to that of other algorithms, the EAOA can effectively balance the exploration and the exploitation to obtain the best solution. In addition, the EAOA has a faster convergence rate, higher calculation precision and stronger stability.

A Nonlinear Adaptive Weight-Based Mutated Whale Optimization Algorithm and Its Application for Solving Engineering Problems

“…In order to further demonstrate that the proposed NAWMWOA is superior to other algorithms, five state-ofthe-art algorithms (nonlinear based chaotic harris hawks optimization (NCHHO) [70], grey wolf optimizer algorithm with a two-phase mutation (TMGWO) [71], dispersed foraging slime mould algorithm (DFSMA) [72], incremental grey wolf optimizer (I-GWO) [73], enhanced arithmetic optimization algorithm (EAOA) [74]) are adapted to compare with NAWMWOA, which are variants of the corresponding original algorithm and have better performance than the original algorithm. Therefore, it is a difficult challenge for NAWMWOA.…”

A Comprehensive Survey on Arithmetic Optimization Algorithm

“…The obtained results of the improved method show this method's ability to produce highly competitive solutions. Zhang et al [87] proposed an enhanced variant of AOA called EAOA that is based on Lévy variation and differential sorting variation to increase overall optimization capability and address AOA's limitations. The Lévy variation boosts population variety, broadens the space for optimization, improves the power of global search, and boosts computing accuracy.…”

A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems

“…Fang et al used dynamic inertia weights to improve the exploration exploitation capability of the algorithm and introduced dynamic variance probability coefficients and triangular variance strategies to help the algorithm avoid local optima. Zhang et al used a differential variance ranking strategy to improve the local exploitation capability of AOA [ 23 ]. Abualigah et al mixed AOA with the sine and cosine algorithm to enhance the local search performance of the algorithm [ 24 ].…”