Performance Of Flower Pollination Algorithm Research Articles

A new flower pollination algorithm with improved convergence and its application to engineering optimization

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm that mimics the pollination behaviour of flowering plants. Despite the promising performance of FPA in solving single objective optimization problems, its convergence still poses challenges in practice. This study proposes a modified FPA with additional features from chaos theory and frog leaping algorithm augmented by inertia weights. The modified FPA proposed in this study is tested against benchmark mathematical functions, mechanical engineering design optimization problems, and machining process optimization problems. Performance comparison with other state-of-the-art optimization algorithms has demonstrated its ability in terms of convergence. The modified FPA significantly reduced the number of function evaluations by 84.14%, as compared to FPA in optimizing the benchmark mathematical functions. Besides, the proposed modified FPA outperformed others in 12 out of 15 mechanical engineering optimization problems.

Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.

Vision-based path detection of an automated guided vehicle using flower pollination algorithm

The automated guided vehicle (AGV) with sensor recognition method is having shortcomings such as high cost and noise. In this regard, the flower pollination algorithm (FPA) is applied in the path detection system of an AGV, in combination with computer vision. The path detection system starts with image acquisition using an onboard camera. The captured image is then preprocessed to obtain simple contrast of the path. Subsequently, the FPA is used to find a set of solution points fall inside the path zone. A regression model is formed as path guidance for AGV to travel accordingly. The performance of FPA is analyzed via simulation and real-world experiment using a robotic platform and tested on commonly seen line patterns in the industrial environment. The effectiveness of FPA in path detection is also compared with particle swarm optimization. The obtained results demonstrate the promising feasibility of the proposed FPA-based path detection system.

Synthesis of flat-top beam pattern of linear antenna arrays with restricted side lobe level, VSWR and independent nulls using Flower Pollination algorithm

ABSTRACTInspired by the biological evolution and the natural selection, evolutionary algorithms have already shown their success in obtaining the solutions for many complex optimisation problems. The aim of this paper is to study the performance of Flower Pollination algorithm for the synthesis of the flat-top beam pattern in linear antenna arrays with restrictions on the peak side lobe level, ripple in the flat-top portion, and maximum Voltage Standing Wave Ratio along with the two independent nulls. Mutual coupling is considered between the parallel dipole antennas. This synthesis is obtained by the generation of current amplitudes with binary phase shifters of 0 and 180 degrees for the antenna elements. Matlab software is used for the simulations. The values of the different antenna parameters as well as the statistical parameters such as the global best fitness value, worst fitness value, mean and standard deviation from the optimisation process generated by the simulations are obtained. Two examples are presented in this paper with different number of antenna elements. The obtained results show the superiority of the Flower Pollination algorithm over Particle Swarm Optimisation algorithm. Moreover, the results obtained from simulations are validated by using FEKO software.

Flower Pollination Algorithm for feature analysis of Kyoto 2006+ data set

Unwanted and large features in data contributes to network classification problem. Features can be selected to improve the performance. In this paper, Flower Pollination Algorithm (FPA) has been proposed to select the optimal features. Further, three predefined feature selection algorithms have been used to selects the most critical attributes for anomaly detection. The performance of FPA and three predefined algorithms have been compared on fifteen features of kyoto 2006+ of Intrusion Detection System (IDS). The evaluation results show that ‘Service (2), Srv_serror_rate (8) and Flag (14) features are the most critical features.

Research on the searching performance of flower pollination algorithm with three random walks

Research on the searching performance of flower pollination algorithm with three random walks

Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems

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.

Reactive power performance analysis of dish–Stirling solar thermal–diesel hybrid energy system

Reactive power analysis of an autonomous hybrid energy system consisting of dish–Stirling solar thermal system (DSTS), diesel engine generator and static VAR compensator (SVC) has been conducted. Diesel engine coupled to a synchronous generator equipped with automatic voltage regulator (AVR) and DSTS is connected to an induction generator. The parameters of the proportional–integral controllers, employed with SVC and AVR are optimised simultaneously using genetic algorithm (GA), particle swarm optimisation (PSO) and flower pollination algorithm (FPA) techniques. The comparative performance of GA, PSO and FPA optimised controllers on the hybrid system model has been presented considering step change and random variations of solar thermal power as well as reactive power load. Simulation results revealed that FPA optimised controllers for AVR and SVC can provide the improved dynamic performance of the hybrid energy system as compared with GA and PSO optimised controllers.

A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC)

Over a period of years, Maximum Power Point Tracking has become a mandatory requirement for Solar Photo Voltaic (PV) systems. Being dependent to environmental changes, the PV power constantly fluctuates due to change in irradiation. Under such conditions, large PV array connected in interconnection will experience non-uniform irradiation thus results multiple peaks in P-V characteristics. Although many conventional and soft computing techniques have been proposed in literature, the ability to identify global peak under strong shading conditions is not guaranteed. Particularly, local peak in close agreement to global peak makes most of the algorithms to get trapped in local peaks. This condition often occurs due to insufficient randomness in algorithm hence, a new Flower Pollination Algorithm (FPA) is investigated in this research. Proposed method has dual mode search ability which creates required randomness in every iteration is the key reason to suit FPA for MPPT. Simulation and experimental results verified with different patterns portray FPA excellence under all irradiated conditions. Further performance of FPA is verified with Particle swarm Optimization method and conventional P&O method.

Assembly sequence optimization using a flower pollination algorithm-based approach

One of the important decisions in assembly process planning is determination of assembly sequence. Choice of the optimum sequence is made difficult due to various reasons. There are various precedence constraints and optimization criteria. Moreover, a product may be possible to assemble in many alternative ways following different sequences, thus making assembly sequence optimization a multi-modal optimization problem with multiple optimum solutions. It is necessary to generate as many unique optimum solutions as possible in order to allow the process planner to take a decision. Moreover, with increase in part count, the number of feasible sequences rises staggeringly, thereby making assembly sequence optimization laborious and time consuming. Most conventional mathematical algorithms are known to perform poorly when used to obtain multiple optimum solutions. On the other hand, soft computing based evolutionary optimization algorithms are good candidates for multi-modal optimization. Another challenge is to develop an algorithm that can automatically maintain diversity in the optimum solutions found over the generations (i.e. optimum solutions having the same fitness but unique). Keeping the above in mind, in the present paper, an intelligent assembly sequence optimization methodology based on application of flower pollination algorithm (FPA) has been developed to automatically generate multiple unique optimal assembly sequences, subject to various precedence constraints, based on minimisation of number of orientation changes and tool changes. Since in the present paper, FPA has been applied for the first time to a discrete optimization problem like assembly sequence optimization, the main challenge before us in applying FPA was the continuous nature of the original FPA. Therefore, modifications have been made by us in the rules for local and global pollination of FPA to make it suited for solving the given discrete optimization problem. In order to evaluate the performance of FPA, the results have been compared with two other well-known soft computing techniques namely, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) and also with a recently published soft computing based algorithm, Improved Harmony Search (IHS). It was found that the novelty of the proposed FPA lies in its capability to find multiple unique optimum solutions in one single simulation run and capability to automatically maintain diversity in the optimum solutions found over the generations. On the other hand, in case of GA, ACO and IHS, it is not possible to maintain the diversity in multiple optimum solutions as the complete population finally converges to a few unique optimum solutions. Therefore, it can be concluded that FPA performs better in solving the given multi-modal optimization problem of assembly sequence optimization.

A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition.