Application of Modified Flower Pollination Algorithm on Mechanical Engineering Design Problem

Meng, Ong Kok; Ong, Pauline; Kiong, Sia Chee; Wahab, Hanani Abdul; Jafferi, Noormaziah

doi:10.1088/1757-899x/165/1/012032

Cited by 19 publications

(12 citation statements)

References 17 publications

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“…Kanagasabai et al [69] integrated FPA with PSO (FPAPSO) to deal with reactive power dispatch problems. Meng et al [70] presented a modified flower pollination algorithm (MFPA) while attempting to optimize five mechanical engineering design problems. Binh et al [71] proposed the Chaotic Flower Pollination Algorithm (CFPA) to overcome the large computation time and solution instability of FPA.…”

Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…The Chaos Modified Flower Pollination Algorithm (CHAOS-MFPA) makes use of chaotic maps to replace the random sequences that generate the initial population in the FPA [49]. This improves the diversity of the initial population, by making the distribution more uniform [49]. Meng et al [49] implemented and tested the ability of the CHAOS-MFPA to solve a variety of mechanical engineering design problems.…”

Section: Chaos Modified Flower Pollination Algorithm (Chaos-mfpa)mentioning

confidence: 99%

“…This improves the diversity of the initial population, by making the distribution more uniform [49]. Meng et al [49] implemented and tested the ability of the CHAOS-MFPA to solve a variety of mechanical engineering design problems. It was found that the non-repetition of the chaotic sequences allowed for optimal solutions to be found in each case.…”

Section: Chaos Modified Flower Pollination Algorithm (Chaos-mfpa)mentioning

confidence: 99%

“…The algorithm is guided by the norm pattern of evolutionary algorithms and is made up of steps for each iteration. Chakraborty et al [49] presented a hybridized FPA which combined the DE algorithm with FPA and used five test functions to compare this hybridized algorithm with its individual counterparts. This combination of DE and FPA was made possible since the DE algorithm can find optima among erratically chosen trajectories but lacks the ability to move towards the global optimum.…”

Section: ) Differential Evolutionmentioning

confidence: 99%

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Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

et al. 2022

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“…Rodrigues et al developed a binary flower pollination algorithm (BFPA) for feature selection, the results showed that BFPA provided better results than PSO, HSA and FA [25]. Metwalli et al proposed a chaosbased flower pollination algorithm (CFPA) to solve fractional programming problems in which the chaos theory in the enhancement of local search, the experimental results showed that the performance of CFPA is better than others algorithms [26]. Shen et al presented a modified FPA (MFPA) to searching the optimal multilevel thresholding.…”

Section: Introductionmentioning

confidence: 99%

An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding

Tan

2019

IEEE Access

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Multilevel image thresholding is an important technique for image processing. However, the computational complexity of multilevel image thresholding grows exponentially with the increase in the number of thresholds when using the exhaustive searching method. To address this problem, a plenty of heuristic algorithms are applied to search the optimal thresholds. In this paper, an improved flower pollination algorithm (IFPA) using Tsallis entropy as its objective function is presented to find the optimal multilevel thresholding. In the IFPA, three modifications are utilized to enhance the flower pollination algorithm (FPA). First, an adaptive switch probability method is used to balance the local and global pollination. Second, a new local pollination strategy is adopted to avoid the population falling into local optimum. Third, an crossover and selection operations are applied to the FPA which can increase the diversity of the population, then enhancing the performance of the FPA. Subsequently, three different algorithms such as FPA, GSA and DE are introduced to compare with the IFPA in the experiments. The experimental results demonstrated that the IFPA can search out the optimal thresholds effectively, accurately and can obtain the best image segmentation quality. INDEX TERMS Improved flower pollination algorithm, image segmentation, Tsallis entropy, multilevel thresholding.

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Application of Modified Flower Pollination Algorithm on Mechanical Engineering Design Problem

Cited by 19 publications

References 17 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Applications of Flower Pollination Algorithm in Electrical Power Systems: A Review

An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding

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