A Novel Arithmetic Ant Colony Hybrid Optimization Algorithm for Numerical Computing

Abstract

The problem of finding all extremal points of a multimodal function and integrating complex functions are among the challenging problems in the field of intelligent optimization. To solve these numerical computation problems, a hybrid optimization algorithm named AOA-ACO, which combines the arithmetic optimization algorithm (AOA) and the ant colony optimization (ACO), is proposed in this paper. AOA is a newly proposed meta-heuristic algorithm in recent years, which has strong global exploration ability and local exploitation ability, and its optimization process has excellent characteristics of randomness and dispersion. ACO, on the other hand, has the characteristics of distributed computing and uses a positive feedback mechanism, but it is difficult to escape local optimum. Based on the characteristics of these two algorithms, AOA-ACO retains the position updating strategy of AOA and incorporates AOA's multiplication and division strategy and addition and subtraction strategy into the ACO optimization process, to enhance the balance between the algorithm's global exploration ability and local exploitation ability while maintaining its diversity, thereby improving the algorithm's computational accuracy y and convergence speed. The numerical experimental results show that compared with other intelligent optimization algorithms, AOA-ACO can calculate extremal points and integrals more accurately and with faster convergence speed. AMS(2010)68W40