An improved hybrid flower pollination algorithm for assembly sequence optimization

PAPR reduction of asymmetrically clipped optical OFDM signals: Optimizing PTS technique using improved flower pollination algorithm

Condenser heat transfer parameters serve as critical indicators for assessing the operational status of condensers. However, these parameters cannot be directly obtained through measurement or inferred solely from condenser characteristic curves, which limits their practical applicability in performance monitoring. To overcome this limitation, we develop a parameter identification method for condenser heat transfer models based on an improved flower pollination algorithm (FPA). Specifically, a condenser heat transfer model is first formulated, and a fitness function is defined to guide the optimization process. On this basis, three enhancement strategies are introduced: a dynamic pollination probability mechanism, a chaotic initialization scheme, and an elite pollen mutation-crossover operator. These improvements are designed to address inherent shortcomings of the classical FPA, including its tendency to converge prematurely to local optima and its limited fitting performance. Comparative experiments conducted on real operational datasets demonstrate that the proposed improved FPA achieves significantly higher parameter identification accuracy and superior model fitting compared with the classical FPA.

Due to weather changes, the distributed photovoltaic (PV) output power has fluctuations, seriously affecting the power quality of the distribution network. This paper proposes a distributed PV power output fluctuation suppression method based on the improved Flower Pollination Algorithm (FPA) to solve the above problem. This method uses an annihilation perturbation mechanism and linear weights to update the pollen position, which mainly affects the output power of photovoltaic power plants. The time constant of the first-order low-pass filter is optimized, and the improved flower pollination algorithm is utilized for optimization to obtain the optimal time constant of the first-order low-pass filter, suppressing the fluctuation of distributed photovoltaic output and integrating the photovoltaic power station’s output. The simulations indicate that the improved flower pollination algorithm is better than other evaluated algorithms and has an improvement of about 11% over the particle swarm algorithm.

The zero trust systems help to improve the overall security of computer networks, while one of the main challenges of zero trust systems is the construction and optimization of continuous authentication models. Aiming at the shortcomings of the existing authentication performance, this paper proposes a continuous authentication model based on an improved flower pollination algorithm (FPA) and extreme gradient boosting (XGBoost) to improve the accuracy of authentication. The model first uses multiple strategies to optimize FPA to obtain MSFPA; then MSFPA is applied to XGBoost for hyper-parameter tuning to obtain MSFPA-XGBoost; and finally, MSFPA-XGBoost is applied to the user's continuous authentication. Among these approaches, MSFPA incorporates chaotic mapping to enhance the initial population. It also utilizes an adaptive transformation probability strategy to dynamically strike a balance between global and local search. Furthermore, it refines the search equation to optimize both global and local search. Additionally, MSFPA employs a pollen cross-boundary correction strategy and incorporates the concept of cross-mutation to augment the algorithm's exploratory capabilities. The experimental results demonstrate that, in the context of parameter optimization tasks, MSFPA exhibits superior performance compared to other optimization algorithms, specifically in terms of search accuracy and convergence speed. In addition, in terms of continuous identity verification effect, compared with each classification model, MSFPA-XGBoost improves the Accuracy, Recall, Precision, F1-Score, and AUC metrics by an average of 1.84%, 2.49%, 2.33%, 2.39%, and 3.11%, which indicates that the proposed model enhances the accuracy of continuous authentication and is more effectively applicable within zero-trust systems.

In this article, an evolutionary approach with different objectives based on the improvement of the hybrid GSA (golden section search algorithm) using the flower pollination algorithm (FPA) with fitness distance balance selection (FDS) is proposed. In GSFPAFDS algorithm, the optimization problem is improved in several sub-problems. The pollination of flowers in the population is enhanced in Levy flight with fitness distance to enhance convergence. Therefore, the GSA can be used as a local search in FPA. This simulation is part of the traditional GSA, FPA, GSFPA, and personnel processing to solve the consequences of sequential global optimization problems. A local update strategy is used to increase optimization capacity. Its main objectives are to minimize the overall execution time and the allocation of the balance between local and global optimal zone. It allows global parallelization with the simultaneous performance of the processor to the functions, and determine the ratio of the communication to the functions. The behavior of the GSFPAFDS with few methods such as GSA, FPA, and GSFPA understandability has been showcased to solve high-dimensional technical problems.KeywordsGolden section algorithm (GSA)Flower pollination algorithm (FPA)And fitness distance balance

This book chapter begins with an overview of three equivalent circuit models of solar PV such as single diode model (SDM), double diode model (DDM) and three diode model (TDM), as well as critical comparison and analysis of each model. In addition, the extraction and optimisation of the solar PV parameters using various Evolutionary algorithms are discussed in this chapter. The modelling parameters such as the photon current, saturation current, the series and parallel resistances are investigated to understand the optimum value. It will compare the results of datasheet values from PV manufacturers with experimental values obtained from PV module measurements. Furthermore, the meta-heuristics techniques include genetic algorithm (GA), particle swarm optimisation (PSO), harmony search (HS), flower pollination algorithm (FPA), simulated annealing (SA), teaching learning-based optimisation (TLBO), and others will also be investigated.

The flower pollination algorithm (FPA) is a relatively new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Indeed, this algorithm is based globally on two main rules: the global pollination (biotic and cross-pollination) and the local pollination (abiotic and self-pollination). The random permutation between these latter allows to keep a permanent balance between intensification and diversification. However, this procedure causes an involuntary orientation toward a bad solution (local optima). In addition, FPA illustrates an inadequacy in terms of intensification and diversification of new solutions; this has become clear when the complexity of the treated problem is increased. Further, FPA has also another insufficiency, which is its slow convergence rate caused in principle by its weak intensification. In this paper, to overcome these weaknesses, we have introduced some modifications on the basic FPA algorithmic structure based on the two following improvements: (1) Generating a set of global orientations (toward global or local pollination) for all members of the population. Indeed, each element (global orientation) in this set is composed of a fixed number (equal to the population size) of sub-random orientation. Thus, the number of elements is fixed by the designer, which enhances significantly the diversification characteristic. (2) Constructing a set of best solution vectors relating to all generated global orientations. In fact, this set is compared at each iteration to a fixed number of actual solution vectors to select the best among them based on their fitness values. The proposed algorithm called novel modified FPA (NMFPA) with its novel algorithmic structure offers to researchers the opportunity to: (1) use it in their comparison study (e.g., with others FPA proposed variants) and (2) develop other new methods or techniques based on its novel integrated mechanisms. To demonstrate the performance of this new FPA variant, a set of 28 benchmark functions defined in IEEE-CEC’13 and a 15 real-world numerical optimization problems proposed in the IEEE-CEC’11 are employed. Compared with FPA, two its famous variants and other state-of-the-art evolutionary algorithms, NMFPA shows overall better performance.

A species-based flower pollination algorithm with increased selection pressure in abiotic local pollination and enhanced intensification

Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system

This article presents a study on the optimization of parameters of a solid oxide fuel cell (SOFC) by using seven different metaheuristic-based optimization techniques such as Differential Evolutionary algorithm (DE), Genetic Algorithm (GA), Evolutionary Programming (EP), Flower Pollination Algorithm (FPA), Evolutionary Strategies (ES), Memetic Algorithm (MA) and Coral Reefs Optimization (CRO). Optimization of a solid oxide fuel cell which is a complex, non-linear and multimodal problem, is solved by using these algorithms. Voltage-based objective function expressed as minimization of the sum of squared error has been developed in this study. Based on given optimization algorithms, the best value of unknown parameters of solid oxide fuel cell has been reported. Current–voltage, power and efficiency curves are shown in this study to match the estimated values with experimental values. Differential evolutionary algorithm (DE) is found to be best evolutionary algorithm having sum of squared error, mean and standard deviation of 8.33E-06, 1.57E-05 and 6.792E-06 respectively which are minimum among all other algorithm. Differential evolutionary algorithm predicts accurately among all other algorithms, used to find the unknown parameters of solid oxide fuel cell. Box plot shows the minimum median and lowest IQR which further validate the authenticity of DE among all evolutionary based algorithms to find unknown parameters of SOFC used in this study.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

A Modified Flower Pollination Algorithm for Global Optimization

It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms.

A genetic algorithm with a mixed region search for the asymmetric traveling salesman problem