Study of Flower Pollination Algorithm for Continuous Optimization

Łukasik, Szymon; Kowalski, Piotr A.

doi:10.1007/978-3-319-11313-5_40

Cited by 49 publications

(24 citation statements)

References 11 publications

(13 reference statements)

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“…The implementation of the game presented in this paper can be further developed in many different ways. The most interesting directions are to find a better selection of krill algorithm parameters in order to be more efficient; to implement an improved version of the KHA, ie Lévy-flight KHA [37]; or to implement a learning mechanism based on human player experience. …”

Section: Discussionmentioning

confidence: 99%

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On the Use of Nature Inspired Metaheuristic in Computer Game

Kowalski¹,

Łukasik²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-This paper describes the use of a new swarm-based metaheuristic, namely Krill Herd Algorithm (KHA), in computer gaming. In this work, KHA is employed to find a bots movement strategy in a computer racing game. The complete algorithm is implemented using a Unity Engine in C# language. Herein, the triggering of the metaheuristic optimization task was conducted by the way of a KHA internal parameter investigation. In this approach, the goal of the race (the KHA evaluation function) for both the human and computer player is to finish a lap in the shortest time possible.

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Section: Discussionmentioning

confidence: 99%

“…Herein, we saw that the quality of the solution can be greatly influenced by the impact of internal parameters [24], [37].…”

Section: Numerical Simulationsmentioning

confidence: 99%

On the Use of Nature Inspired Metaheuristic in Computer Game

Kowalski¹,

Łukasik²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Self Cite

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show abstract

“…Binh et al [71] proposed the Chaotic Flower Pollination Algorithm (CFPA) to overcome the large computation time and solution instability of FPA. Łukasik et al [72] studied FPA in solving continuous global optimization problems in intelligent systems. Sakib et al [73] also presented a comparative study of FPA and BA in solving continuous global optimization problems.…”

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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“…Besides these basic execution parameters, FPA has other parameters that were not modified, default values are used. Their impact on model performance has been analysed in [12]. The RSS and RSE columns present the best (the least), error value achieved in algorithm executions with given parameter values.…”

Section: Model Constructionmentioning

confidence: 99%

A Database Performance Polynomial Multiple Regression Model

Nowosielski

Kowalski

Kulczycki

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles are discussed and alternative modelling approaches are mentioned.

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Study of Flower Pollination Algorithm for Continuous Optimization

Cited by 49 publications

References 11 publications

On the Use of Nature Inspired Metaheuristic in Computer Game

On the Use of Nature Inspired Metaheuristic in Computer Game

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

A Database Performance Polynomial Multiple Regression Model

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