Abstract:To improve the performance of the arithmetic optimization algorithm (AOA) and solve problems in the AOA, a novel improved AOA using a multi-strategy approach is proposed. Firstly, circle chaotic mapping is used to increase the diversity of the population. Secondly, a math optimizer accelerated (MOA) function optimized by means of a composite cycloid is proposed to improve the convergence speed of the algorithm. Meanwhile, the symmetry of the composite cycloid is used to balance the global search ability in the… Show more
“…Liu et al [102] presented an enhanced AOA, by employing a multi-strategy method to enhance the functionality of the AOA and address issues in the AOA. First, circle chaotic mapping is employed to broaden the population's diversity.…”
Section: Other Utilized Strategies To Improve Aoamentioning
Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications.
“…Liu et al [102] presented an enhanced AOA, by employing a multi-strategy method to enhance the functionality of the AOA and address issues in the AOA. First, circle chaotic mapping is employed to broaden the population's diversity.…”
Section: Other Utilized Strategies To Improve Aoamentioning
Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications.
“…Pashaei et al introduced a hybrid binary AOA with a simulated annealing algorithm to solve the feature selection problem, the proposed algorithm obtained better classification accuracy and optimization results [20]. Liu et al proposed an improved AOA based on circle chaotic mapping, elite mutation approach and Cauchy disturbances to solve the function optimization and the engineering design problems, the optimization results of the proposed algorithm were better than those of other algorithms [21]. Khodadadi et al designed a dynamic AOA to solve the truss optimization problems, the proposed algorithm balanced exploration and exploitation to find the global solution in the search space [22].…”
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.
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