Flower pollination algorithm: A novel approach for multiobjective optimization

Yang, Xin-She; Karamanoglu, Mehmet; He, X. Q.

doi:10.1080/0305215x.2013.832237

Cited by 532 publications

(238 citation statements)

References 40 publications

(32 reference statements)

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“…It is inspired from the natural process 'pollination of flowers'. This metaheuristic algorithm has evolutionary characteristics and its convergence rate is relatively high as compared to other nature inspired algorithms [36].…”

Section: A Flower Pollination Algorithmmentioning

confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

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Section: A Flower Pollination Algorithmmentioning

confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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“…This may be because of the poor exploration ability of BA. In addition, the multidimensional objective problem can affect the convergence rate and the accuracy of the solutions obtained by PFA [17]. This result implies that BA, FPA and also CSA are potentially more efficient in solving low-dimensional computationally-expensive optimization problems.…”

Section: The Accuracy With Limited Number Of Iterationsmentioning

confidence: 98%

“…Sakib et al [73] also presented a comparative study of FPA and BA in solving continuous global optimization problems. The main steps and the mathematical equations of FPA are explained in detail in [17].…”

Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

“…Dubey et al [63] modified FPA to deal with the fuzzy selection of dynamic GO problems. Yang et al [17] extended FPA to solve multi-objective GO problems, developing a multi-objective flower pollination algorithm (MOFPA). Henawy et al [67] introduced a hybrid of FPA combined with harmony search (FPCHS) to increase the accuracy of FPA.…”

Section: Flower Pollination Algorithm Methodsmentioning

confidence: 99%

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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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“…Evolutionary algorithms (EAs) are effective heuristic algorithms for solving global optimization problems [7][8][9][10][11]. Moreover, many kinds of evolutionary algorithms have been successfully applied to solve the 0-1 knapsack problem in the past few decades.…”

Section: Introductionmentioning

confidence: 99%

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Guo

Yue

Zhang

et al. 2014

Entropy

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Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE), we present a novel discrete differential evolution (TDDE) for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances.

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Flower pollination algorithm: A novel approach for multiobjective optimization

Cited by 532 publications

References 40 publications

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

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

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

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