scholarly journals An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260725
Author(s):  
Jiahao Fan ◽  
Ying Li ◽  
Tan Wang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Time Varying ◽  
Metaheuristic Algorithm ◽  
Design Problems ◽  
Chaotic Mapping ◽  
Metaheuristic Optimization ◽  
Engineering Design Problems ◽  
Metaheuristic Optimization Algorithms ◽  
Better Than

Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual’s historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.

scholarly journals Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1774
Author(s):  
Rong Zheng ◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
Shuang Wang
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Algorithms ◽  
Slime Mold ◽  
Heuristic Methods ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Different Dimensions ◽  
Speed And Accuracy

In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.

A probabilistic metric for comparing metaheuristic optimization algorithms

2018 ◽  
Vol 70 ◽  
pp. 59-70 ◽  
Author(s):  
Wellison J.S. Gomes ◽  
André T. Beck ◽  
Rafael H. Lopez ◽  
Leandro F.F. Miguel
Keyword(s):  
Optimization Algorithms ◽  
Metaheuristic Optimization ◽  
Probabilistic Metric ◽  
Metaheuristic Optimization Algorithms

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

A Fuzzy Adaptive Simplex Search Optimization Algorithm

1999 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Xiao Bin Lu
Keyword(s):  
Fuzzy Logic ◽  
Optimization Algorithms ◽  
Test Problems ◽  
Objective Functions ◽  
Fuzzy Logic Controllers ◽  
Design Problems ◽  
Simplex Search ◽  
Search Optimization ◽  
Engineering Design Problems ◽  
Fuzzy Adaptive

Abstract Optimization algorithms usually use fixed parameters that are empirically chosen to reach the minimum for various objective functions. This paper shows how to incorporate fuzzy logic in optimization algorithms to make the search adaptive to various objective functions. This idea is applied to produce a new algorithm for minimization of a function of n variables using an adaptive form of the simplex method. The search starts by generating a simplex with n+1 vertices. The algorithm replaces the point with the highest function value by a new point. This process comprises reflecting the point with the highest function value in addition to expanding or contracting the simplex using fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. The efficiency of the algorithm is studied using a set of standard minimization test problems. This algorithm generally results in a faster convergence toward the minimum. The algorithm is also applied successfully to two engineering design problems.

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

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