Optimizing Reinforced Cantilever Retaining Walls Under Dynamic Loading Using Improved Flower Pollination Algorithm

2021 ◽  
pp. 139-169
Author(s):  
E. B. Tutuş ◽  
O. Pekcan ◽  
M. Altun ◽  
M. Türkezer
Keyword(s):  
Dynamic Loading ◽  
Retaining Walls ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Cantilever Retaining Walls

scholarly journals Optimal Design of Reinforced Concrete Cantilever Retaining Walls Utilizing Eleven Meta-Heuristic Algorithms: A Comparative Study

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Kiarash Biabani Hamedani ◽  
Taha Bakhshpoori
Keyword(s):  
Reinforced Concrete ◽  
Dynamic Loading ◽  
Optimization Algorithm ◽  
Loading Condition ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Retaining Wall ◽  
Retaining Walls ◽  
Water Evaporation ◽  
Cantilever Retaining Walls

In this paper, optimum design of reinforced concrete cantilever retaining walls is performed under static and dynamic loading conditions utilizing eleven population-based meta-heuristic algorithms. These algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Imperialist Competitive Algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Ray Optimization algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, Vibrating Particles System algorithm, and Cyclical Parthenogenesis Algorithm. Two well-known methods consisting of the Rankine and Coulomb methods are used to determine lateral earth pressures acting on cantilever retaining wall under static loading condition. In addition, Mononobe-Okabe method is employed for dynamic loading condition. The design is based on ACI 318-05 and the goal of optimization is to minimize the cost function of the cantilever retaining wall. The performance of the utilized algorithms is investigated through an optimization example of cantilever retaining wall. In addition, convergence histories of the algorithms are provided for better understanding of their performance.

Maiden application of flower pollination algorithm based tilt integral derivative controller with filter for control of electric machines

2021 ◽  
Author(s):  
Tirumalasetty Chiranjeevi ◽  
N.Ram Babu ◽  
S.K. Pandey ◽  
Raj Kumar Patel ◽  
Umesh Kumar Gupta ◽  
...  
Keyword(s):  
Electric Machines ◽  
Flower Pollination Algorithm ◽  
Flower Pollination

scholarly journals Modified Flower Pollination Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1661
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Safaa Saber ◽  
S. S. Askar ◽  
Mohamed Abouhawwash
Keyword(s):  
Global Optimization ◽  
Nonlinear Equation ◽  
Differential Evolution ◽  
Flower Pollination Algorithm ◽  
Test Cases ◽  
Test Functions ◽  
Flower Pollination ◽  
New Variant ◽  
Experimental Findings ◽  
Nonlinear Equation Systems

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

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