Metaheuristic algorithms

In financial markets such as Tehran Stock Exchange, P/E coefficient, which is one of the most well-known instruments for evaluating stock prices in financial markets, is considered necessary for shareholders, investors, analysts and corporate executives. P/E is used as an important indicator in investment decisions. In this research, harmony search metaheuristic algorithm is used to select optimal variables affecting P/E and then, modelling is done through multivariate regression based on panel data. For this purpose, a sample of 87 companies has been selected from listed companies in the Tehran Stock Exchange during a 10-year period (2006-2015). The results indicate the effect of the variables of stock returns, stock price to book value ratio, price to net selling ratio, return on assets, earnings per share, market value to book value, money volume, operating return margin, return on capital, and current assets, as top ten variables, on P/E ratio, which estimates a total of 86% of the P/E ratio changes.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.

Purpose – The purpose of this paper is to optimize Cost & time in the three echelon supply chain (SC) network. This paper developed a linear programing (LP) model to consider economic data. Design/methodology/approach – The overall objective of the present study is to use high-quality raw materials, at the same time in post COVID 19 pandemic and the highest profitability is achieved. The model in the problem is solved using two metaheuristic algorithms, namely, Cuckoo and Genetic. Optimization of supply chain performance indicators in minimization of cost and time and maximization of sustainability indexes of the system. Findings – The differences found between the genetic algorithms (GAs) and the LP approaches can be explained by handling the constraints and their various logics. To deal with ambiguity in the reverse logistics network, a fuzzy approach has been applied. To solve the problem in large dimensions, meta-heuristic algorithms of Cuckoo and Genetic were employed by applying MATLAB software. In order to compare two optimization algorithms, a series of sample problems have been generated then the results of two algorithms were compared and superiority of each of them was discussed.