This article presents a comprehensive review of chaos embedded meta-Heuristic optimization Algorithms and describes the evolution of this Algorithms along with some improvements, their combination with various methods as well as their applications. The reported results indicate that chaos embedded Algorithms may handle engineering design problems efficiently in terms of precision and convergence and, in most cases; they outperform the results presented in the previous works. The main goal of this paper is to providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.

The main advantage of Heuristic or metaHeuristic Algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaHeuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of Heuristic and metaHeuristic Algorithms have a great influence on their effectiveness and efficiency, parameter tuning and calibration has gained importance. In this paper a new approach for robust parameter tuning of Heuristics and metaHeuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.

Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony Algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony Algorithms in terms of portfolio formation.

Energy production and consumption play an important role in the domestic and international strategic decisions globally. Monitoring the electric energy consumption is essential for the short- and long-term of sustainable development planned in different countries. One of the advanced methods and/or Algorithms applied in this prediction is the meta-Heuristic algorithm. The meta-Heuristic Algorithms can minimize the errors and standard deviations in the data processing. Statistically, there are numerous methods applicable in the uncertainty analysis and in realizing the errors in the datasets, if any. In this article, the Mean Absolute Percentage Error (MAPE) is used in the error’s minimization within the relevant Algorithms, and the used dataset is actually relating to the past fifty years, say from 1972 to 2021. For this purpose, the three Algorithms such as the Imputation–Regularized Optimization (IRO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO) have been used. Each one of the Algorithms has been implemented for the two linear and exponential models. Among this combination of the six models, the linear model of the ECBO meta-Heuristic algorithm has yielded the least error. The magnitude of this error is about 3.7%. The predicted energy consumption with the winning model planned for the year 2030 is about 459 terawatt-hours. The important socio-economical parameters are used in predicting the energy consumption, where these parameters include the electricity price, Gross Domestic Product (GDP), previous year's consumption, and also the population. Application of the meta-Heuristic Algorithms could help the electricity generation industries to calculate the energy consumption of the approaching years with the least error. Researchers should use various Algorithms to minimize this error and make the more realistic prediction.

Loop layout is a common layout in Flexible Manufacturing Systems (FMSs), in which machines are arranged around the loop and materials are transported in a unidirectional route only. The objective of the Loop Layout Problem (LLP) is to regulate machines around a loop to minimize the maximum congestion within a set of parts. Arti, cial Immune System (AIS), Tabu Search (TS), and Improved Tabu Search (ITS) Algorithms are employed to solve these loop layout problems. The Algorithms are tested and validated through large-and small-sized randomly generated hypothetical problems with a minimum required machine sequence. The e, ciency of Algorithms is compared with that of existing Algorithms for benchmark problems. Computational results reveal that ITS algorithm outperforms AIS, TS, and existing algorithm for large-sized hypothetical problems.

The stock market involves risks and returns that, if forecasted correctly, can lead to profitability, and for this forecasting, appropriate methods are needed. It is affected by various parameters and needs a way to identify these parameters well and have a dynamic nature. The main goal of this article is forecasting Tehran Price Index (TEPIX) by using hybrid Artificial Neural Network (ANN) based on Genetic Algorithm (GA), Harmony Search (HS) particle Swarm Optimization algorithm (PSO) Moth Flame Optimization (MFO) and Whale Optimization Algorithms. GA is used as feature selection. So, PSO, HS MFO and WOA are used to determine the number of input and hidden layers. We use the daily values of the stock price index of the Tehran Stock Exchange from 2013 to 2018 in order to forecasting price and test it. The accuracy of ANN, hybrid Artificial Neural Network with HS, PSO MFO and WOA is evaluated based on different loss functions such as MSE, MAE and etc. the results show that the predictability of Meta-Heuristic Algorithms in testing period is higher than normal ANN. Also, the predictability of hybrid WOA is higher than hybrid PSO and HS Algorithms and MFO.