Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems

被引:32
作者
Abualigah, Laith [1 ,2 ]
Ewees, Ahmed A. [3 ]
Al-qaness, Mohammed A. A. [4 ]
Abd Elaziz, Mohamed [5 ,8 ,9 ]
Yousri, Dalia [6 ]
Ibrahim, Rehab Ali [5 ]
Altalhi, Maryam [7 ]
机构
[1] Amman Arab Univ, Fac Comp Sci & Informat, Amman 11953, Jordan
[2] Univ Sains Malaysia, Sch Comp Sci, Pulau 11800, Pinang, Malaysia
[3] Damietta Univ, Dept Comp, Dumyat 34517, Egypt
[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[5] Zagazig Univ, Fac Sci, Dept Math, Zagazig 44519, Egypt
[6] Fayoum Univ, Fac Engn, Dept Elect Engn, Al Fayyum, Egypt
[7] Taif Univ, Coll Business Adm, Dept Management Informat Syst, POB 11099, At Taif 21944, Saudi Arabia
[8] Galala Univ, Fac Comp Sci & Engn, Suze 435611, Egypt
[9] Ajman Univ, Artificial Intelligence Res Ctr AIRC, Ajman 346, U Arab Emirates
关键词
Arithmetic optimization algorithm (AOA); Sine cosine algorithms; Levy flight distribution; Engineering design problems; CEC benchmarks; Optimization problems; PARTICLE SWARM OPTIMIZATION; ANT COLONY OPTIMIZATION; HARMONY SEARCH ALGORITHM; DIFFERENTIAL EVOLUTION; OPTIMAL-DESIGN; INTELLIGENCE; STRATEGY; SELECTION;
D O I
10.1007/s00521-022-06906-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Several metaheuristic methods have been applied to tackling various global and engineering optimization problems. However, this method still needs more improvement since they require a suitable balance between exploration and exploitation. Therefore, this study presents an enhancement of the arithmetic optimization algorithm (AOA) as a global optimization method. The developed method, named AOASC, depends on using the sine-cosine algorithm's operators to enhance the exploitation ability of AOA during the searching process. This leads to improving the convergence rate of the developed method toward the optimal solution. Besides, improve the process of avoiding the attraction toward the local point. Besides these behaviors, the quality of the final solution (best one) is improved. To validate the efficiency of the developed method, a set of experiments is conducted, including various optimization problems, such as ten benchmark functions and five engineering optimization problems. Besides, the results of the developed method are compared with other well-known metaheuristic methods. The results showed the high efficiency of the developed method over other methods in terms of performance measures.
引用
收藏
页码:8823 / 8852
页数:30
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