Comment and improvement on “A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example”

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

Using Fruit Fly Optimization Algorithm (FOA) to optimize the Proportion Integration (PI) control parameters of the single-phase inverter. The transfer function model of the single-phase inverter is obtained by the state space averaging method, and the globally optimum solution to parameters of the PI controller has been searched by using FOA with total harmonic distortion (THD), gain margin and phase margin of the system open-loop function as optimal target functions. Compared with the initial system, the optimal target function marks optimized system has a better control stability; also, the waveform of its output voltage has a smaller THD. According to the Matrix Laboratory (MATLAB) simulation result, it shows that FOA possesses faster convergence speed than Particle Swarm Optimization (PSO), and FOA can effectively find out the globally optimum solution. Upon applying the optimized PI controller to a single-phase inverter, the system has a good stability with a small THD of the output voltage waveform. Thus, the feasibility and effectiveness of applying FOA to the control optimization of the power electronics system can be proven.

A clustering algorithm is a solution for grouping a set of objects and for distribution centre location problems. But the common K-means clustering algorithm may give local optimal solutions. Swarm intelligent algorithms simulate the social behaviours of animals and avoid local optimal solutions. We employ three swarm intelligent algorithms to avoid these solutions. We propose a new algorithm for the clustering problem, the fruit-fly optimization K-means algorithm (FOA K-means). We designed a distribution centre location problem and three clustering indicators to evaluate the performance of algorithms. We compare the algorithms of K-means with the ant colony optimization algorithm (ACO K-means), particle swarm optimization algorithm (PSO K-means), and fruit-fly optimization algorithm. We find K-Means modified by the fruit-fly optimization algorithm (FOA K-means) has the best performance on convergence speed and three clustering indicators, compactness, separation, and integration. Thus, we can apply FOA K-means to improve the distribution centre location solution and the efficiency for distribution in the future.

Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture, thus we set up a prediction model to determine the future changing trend of dissolved oxygen. The dissolved oxygen prediction model we proposed was based on the least squares support vector regression (LSSVR) model with fruit fly optimization algorithm (FOA) to find optimal parameters (γ and σ) of LSSVR. Because these two parameters can significantly affect the performance of the LSSVR, we studied the other two parameter optimization methods the particle swarm optimization (PSO) algorithm, the genetic algorithm (GA) and immune genetic algorithm(IGA) to compare them with the FOA algorithm. The calculated mean absolute percentage errors of the results of the four prediction models were 0.35%, 1.3%, 2.03% and 1.33%, respectively. The FOA-LSSVR model has a higher prediction accuracy and more reliable performance than the other models. When the predicted values of dissolved oxygen fall below the safety level, the farmer can start an oxygen increasing machine in advance to maintain the safety of fishes. The prediction model was used in Yangzhong, Jiangsu province, China, and it performed well and helped farmers make decisions and reduce aquaculture risks.

Purpose – In order to avoid enterprise crisis and cause the domino effect, which influences the investment return of investors, the national economy, and financial crisis, establishing a complete set of feasible financial early warning model can help to prevent the possibility of enterprise crisis in advance, and thus, reduce the influence on society and the economy. The purpose of this paper is to develop an efficient financial crisis warning model. Design/methodology/approach – First, the fruit fly optimization algorithm (FOA) is used to adjust the coefficients of the parameters in the ZSCORE model (we call it the FOA_ZSCORE model), and the difference between the forecasted value and the real target value is calculated. Afterward, the generalized regressive neural network (GRNN model), with optimized spread by FOA (we call it FOA_GRNN model), is used to forecast the difference to promote the forecasting accuracy. Various models, including ZSCORE, FOA_ZSCORE, FOA_ZSCORE+GRNN, and FOA_ZSCORE+FOA_GRNN, are trained and tested. Finally, different models are compared based on their prediction accuracies and ROC curves. Furthermore, more appropriate parameters, which are different from the parameters in the original ZSCORE model, are selected by using the multivariate adaptive regression splines (MARS) method. Findings – The hybrid model of the FOA_ZSCORE together with the FOA_GRNN offers the highest prediction accuracy, compared to other models; the MARS can be used to select more appropriate parameters to further improve the performance of the prediction models. Originality/value – This paper proposes a hybrid model, FOA_ZSCORE+FOA_GRNN which offers better performance than the original ZSCORE model.

Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN

A cloud model based fruit fly optimization algorithm

As a novel evolutionary algorithm, fruit fly optimization algorithm (FOA) has received great attentions and wide applications in recent years. However, existing literature have demonstrated that the basic FOA often risks getting prematurely stuck in the local optima. In this paper, an improved FOA, named as dynamic generalized opposition-based learning fruit fly optimization algorithm (DGOBL-FOA), is proposed to mitigate the aforementioned drawback hence improve the optimization performance. Three carefully designed operators are incorporated into the basic FOA, i.e., a cloud model based osphresis search is applied to enhance the local refinement search ability in the osphresis phase, then a generalized opposition-based learning operation is adopted to strengthen the global coarse search ability, meanwhile a dynamic shrinking parameter strategy is designed to adjust the learning intensity and narrow down the search space iteratively, which contributes to a good balance between the global exploration and local exploitation. To verify the effectiveness of the proposed algorithm, numerical experiments are conducted on 18 well-studied benchmark functions with dimension of 30. The computation results and statistical analysis indicate that the proposed DGOBL-FOA achieve significantly better performance comparing to other FOA variants and the state-of-the-art metaheuristics.

This paper presents a modified fruit fly optimization algorithm(FOA). The proposed modified FOA establishes a balanced tradeoff between exploration and exploitation, and thus overcomes original FOA's drawbacks of premature convergence and easy trapping in a local optima. In the proposed modified FOA, firstly, the whole population performs a global search; Secondly, the whole population are sequenced in descending order by the individual fitness value; Thirdly, every n consecutive individuals are divided into a meme group, then every meme group iteratively performs a deep search around the local optima; Finally, all the meme groups are mixed, and then the above process is implemented iteratively until meeting the end conditions. The modified FOA efficiently avoids relapsing into local optima and improves convergence precision. Finally, our modified algorithm was validated against the original by testing on six standard benchmark functions, and comparisons show that the performance of the proposed modified FOA is much better than original FOA.

To address the issues of local optima trapping and slow convergence in the Fruit Fly Optimization Algorithm (FOA), an enhanced approach called Membrane Computing-based Fruit Fly Optimization Algorithm (MCFOA) is proposed to optimize models. The MCFOA algorithm merges the parallelism of membrane computing with the efficient search capabilities of the fruit fly optimization algorithm, thereby enhancing both search accuracy and convergence speed. First, the improved fruit fly optimization algorithm was built by the cell-like P system consisting of three cells to find the optimal initial weights and thresholds for the BP neural network, to improve the accuracy of grain temperature prediction. In addition, the three cells used different fruit fly optimization algorithms as their evolutionary mechanisms. Under the control of evolutionary mechanisms and communication mechanisms, the model was applied to improve the convergence performance of the algorithm, overcome the defect of falling into local optimal, and continuously optimize the weights and thresholds of the BP neural network to achieve the aiming of improving the prediction accuracy. The results showed that the prediction curve of the MCFOA-BP model is closer to the actual grain temperature curve, and the prediction performance of the MCFOA-BP model is better than other models.

This study offers a new Red Deer Algorithm (RDA) optimization technique for total harmonic distortion (THD) minimization in multilevel inverters (MLIs). RDA belongs to the class of swarm-based biologically inspired optimization methods. To test the angles determined via RDA optimization, a single-phase seven-level cascade multi-level inverter with symmetrical DC sources was used in this study. The switching angles for the optimally minimized total harmonic distortion (OMTHD) were computed via RDA optimization. RDA optimization was compared to meta-heuristic algorithms like the Improved Whale Optimization Algorithm (IWOA), Whale Optimization Algorithm (WOA), Sunflower Optimizer (SFO), Particle Swarm Optimization (PSO), Krill Herd (KH), Grey Wolf Optimizer (GWO), Galactic Swarm Optimization (GSA), Genetic Algorithm (GA), Fruit Fly Optimization Algorithm (FFO) and Artificial Greyctric Field Algorithm (AEFA). The RDA optimization outperformed the ten algorithms used as benchmark frameworks in this study as it can find angles with minimum THD for the modulation index in 0–1.

Advancements in machine learning techniques for coal and gas outburst prediction in underground mines

To design a variety of algorithms and problem-solving techniques, including different ways of searching, designing, and optimization. Evolutionary computation gets the principles or inspirations by observing the nature, organisms, and biological phenomena. Nowadays, many different algorithms, like genetic algorithm (GA), ant algorithm (AA) , particle swarm optimization(PSO), artificial fish swarm algorithm(AFSA), and the latest New fruit fly optimization algorithm(NewFOA), have been developed. In this paper, we adopted newer algorithms, which are PSO, AFSA and NewFOA, to optimize the parameters of general regression neural network(GRNN). Then, by applying some data mining techniques like General Regression Neural Network, we constructed the logistic flow service satisfaction detection model for electronic commerce enterprises. The results indicated that the one hundred and twentieth interviewee showed the highest and the thirteenth the lowest satisfaction in the data envelopment analysis(DEA). We chose four principle components to be the independent variables for our models. In the three data mining techniques, we found out that the root mean square error (RMSE) value of GRNN model which was optimized by NewFOA showed good convergent result and good capability of classification and forecasting.

The application of meta-heuristic algorithms has significant potential in various fields, including wireless sensor networks. In this paper, we utilize two algorithms, the Fruitfly optimization algorithm (FOA) and the Nutcracker optimization algorithm (NOA), to address two critical issues: optimizing coverage and ensuring connectivity in sensor networks. The main contribution of this paper is the application of these algorithms to arbitrary communication radius, independent of predefined connectivity assumptions. Simulation results demonstrate the effectiveness of the proposed methods by comparing with each other and with the two traditional algorithms Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Additionally, this paper simulates the coverage area in an environment with different types of obstacles to showcase the practical flexibility of the algorithms.

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.