Grey Wolf Optimizer

The Whale Optimization Algorithm

Salp Swarm Algorithm

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

Slime mould algorithm: A new method for stochastic optimization

The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization Algorithm. In this article, throughout the process of finding the maximal value and minimal value of a function, the function of this algorithm is tested repeatedly, in the mean time, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwan's enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression are adopted to construct a financial distress model. It is found in this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression Neural Network model has a very good convergence, and the model also has a very good classification and prediction capability.

A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example

The Fruit Fly Optimization Algorithm (FOA) is a widely used intelligent evolutionary algorithm with a simple structure that requires only simple parameters. However, its limited search space and the swarm diversity weaken its global search ability. To tackle this limitation, this paper proposes a novel Multi-Scale cooperative mutation Fruit Fly Optimization Algorithm (MSFOA). First, we analyze the convergence of FOA theoretically and demonstrate that its convergence depends on the initial location of the swarm. Second, a Multi-Scale Cooperative Mutation (MSCM) mechanism is introduced that tackles the limitation of local optimum. Finally, the effectiveness of MSFOA is evaluated based on 29 benchmark functions. The experimental results show that MSFOA significantly outperforms the improved versions of FOA presented in recent literature, including IFFO, CFOA, and CMFOA, on most benchmark functions.

A novel multi-scale cooperative mutation Fruit Fly Optimization Algorithm

In recent years, the automation and electronic system in the logistics industr y have become popular topics in management, which consist of five segments, including marketing, logistics, information technology, banking system, and service system in the online stores (B2C & C2C) of the E-Commerce system. This study contains questionnaires and collective information that focus on logistics. In this article, the results of the survey questionnaires regarding the serv ice quality level of the e-business seller will be used first to conduct the Principal Components Analysis; then the FOA Optimized Grey Model Neural Network(FOAGMNN), the Grey Model Neural Network, and Multiple Regression will be further utilized to perform the construction of service satisfaction detection models. Based on the analysis results in this article, the FOAGMNN model has the fastest error convergence and the best classification forecast capability.

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Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service

Taiwan stock market trend is fast changing It is affected by not only the individual investors and the three major institutional investors, but also impacted by domestic political and economic situations Therefore, to precisely grasp the stock market movement, one must build a perfect stock forecast model In this article, we used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market First, data of 2 stocks, TSMC and UMC were collected, and then inputted the test data into the genetic programing and built a model to find out the arithmetic expressions Artificial Fish Swarm Algorithm is used to dynamically adjust the variable factors and constant factors in the arithmetic expressions Next, we took the error term (e) in arithmetic expressions to Gray Model Neural Network to make the forecast Finally, we used the Artificial Fish Swarm Algorithm to dynamically adjust the parameters of the Gray Model Neural Network to enhance the precision of the stock forecast model as a whole The result showed that the forecast capability of each stage after the optimization process is better than that of its previous stage, and the mixed stock forecast model (GP–AFSA+GMNN–AFSA) in stage 4 greatly enhanced the precision of the forecast

Using multi-stage data mining technique to build forecast model for Taiwan stocks

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating i...

Wen-Tsao Pan

Papers

A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example

A novel multi-scale cooperative mutation Fruit Fly Optimization Algorithm

Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service

Using multi-stage data mining technique to build forecast model for Taiwan stocks

Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model