Variants of the Flower Pollination Algorithm: A Review

Alyasseri, Zaid Abdi Alkareem; Khader, Ahamad Tajudin; Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.; Yang, Xin‐She

doi:10.1007/978-3-319-67669-2_5

Cited by 94 publications

(52 citation statements)

References 81 publications

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“…Global pollen due to some external factors such as wind. Local pollen has a significant fraction in overall pollination activities to show with exemplifying the mechanism of FPA based in there four rules [12].…”

Section: Pollinationmentioning

confidence: 97%

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Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Sambariya¹,

Nagar²,

Sharma³

2020

ujca

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In power system, frequency is one of the main factor which affects the A.C. system. In order to maintain the performance of the system, frequency need to be controlled on the generation part as well as load side. In this paper, fractional order PID (FOPID) controller is designed for the three-area load frequency control (LFC) in an interrelated power system (PS) in MATLAB simulation. The LFC of generating units have used in frequency control of the dissolute response in the power system. FOPID controller parameters are intended using the Flower Pollination Algorithm (FPA) for the minimize error. The prototype of generating units with FOPID controller is simulated in MATLAB/SIMULINK platform. In this study, simulation researches on a three-area LFC with different generating units are considered. The objective function is the minimization of the Integral Squared Error (ISE) for the optimum strategy of FOPID parameters. These results presented that the FOPID controller was vital to the parameter variations in the considered system. The simulation results specialized much better performance of FOPID controller for LFC in comparison with other FOPID controllers available in the literature. FPA-FOPID Controller in the system as compared to the existing ICA-FOPID and GA-FOPID controller in literature. The result shows that the FPA result outperforms as compared with other results.

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Section: Pollinationmentioning

confidence: 97%

“…Yang, 2012 [11] have presented the FPA technique. Alyasseri, 2018 [12] have presented the FPA technique and tuned the FOPID parameters. The switched power between persist zone within the stated parameters and steady-state error zero are also called the LFC [2,8].…”

Section: Introductionmentioning

confidence: 99%

Application of FOPID Design for LFC Using Flower Pollination Algorithm for Three-Area Power System

Sambariya¹,

Nagar²,

Sharma³

2020

ujca

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“…There are many nature-inspired algorithms in the current literature, it is estimated there are more than 100 different algorithms and variants (Reyes-Sierra and Coello Coello, 2006;Kennedy et al, 2001;Price et al, 2005;Fister et al, 2013;Alyasseri et al, 2018;Abdel-Basset and Shawky, 2019). It is not our intention to review all of them.…”

Section: Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

“…However, their main differences are in the ways of generating ∆x t i and ∆v t i from the population of the existing solutions. Most recently, new variants and applications are appearing regularly, including local ant system for allocating robot swarms [Khaluf et al(2019)Khaluf, Vanhee, and Simoens], hybrid ant and firefly algorithms [Goel and Maini(2018)], usability feature selection by MBBAT [Gupta and Ahlawat(2017)], vehicle routing (Osaba et al, 2017;Osaba et al, 2019) and others (Alyasseri et al, 2018;Abdel-Basset and Shawky, 2019).…”

Section: Equation-based Algorithmsmentioning

confidence: 99%

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Nature-inspired optimization algorithms: Challenges and open problems

Yang

2020

Journal of Computational Science

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275

117

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Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.

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