Many researchers proved that hybrid models have better results in comparison with independent models. A combination of different methods could enhance the accuracy of time series prediction. Hence, this research used the hybrid of three methods of Chaos Theory, multi-layer perceptron and Metaheuristic Algorithm to increase the power of the model forecasting. Artificial neural networks have properly considered complex nonlinear relations and are good comprehensive approximators. Multi-objective evolutionary Algorithms such as multi-objective particle swarm optimization are good at solving multi-objective optimization issues. This Algorithm organized the combination of parent and children populations by elitist strategy, decreased the messy comparing factors to improve the solution variety and avoided to use of niche factors. Chaos Theory controls the complexities of stochastic systems. So, this research offers Tehran Stock Exchange Index (TSEI) prediction by a hybrid model of Chaos Theory, multi-layer perceptron and Metaheuristic Algorithm. The results show that in perceptron-based mode, RMSE measures are gradually increased in all intervals. The continuous decrease of RMSE shows that the perceptron-based model could show consistency with the whole data flow. This matter could offer a better learning and consistency process by perceptron-based models to predict stock prices, as this type of learning could apply more experiences for forecasting future behaviour in order to change the system content.

A Hyper Spherical Search (HSS) optimization Algorithm based on Chaos Theory is proposed that resolves the weakness of the standard HSS optimization Algorithm including the speed of convergence and the sequential increment in the number of Algorithm iterations to achieve the optimal solution. For this, in the particle initiation and search steps of the proposed Algorithm, random values used in the standard Algorithm are replaced with the values of two mappings, Chebyshev and Liebovitch, that makes the results of the proposed Algorithm definite and decreases their standard deviation. The simulation results on the standard benchmark functions show that the proposed Algorithm not only has faster convergence, but also acts as a more accurate search Algorithm to find the optimal solution in comparison to standard hyper spherical search Algorithm and some other optimization Algorithms such as genetic, particle swarm, and harmony search Algorithm.

A hybrid optimization Algorithm based on genetic Algorithm and choatic hyper spherical search method is proposed. In the proposed method, in order to increase the efficiency of searching the optimal solution, Chaos Theory along with genetic operators have been used. This, not only makes the results of the proposed Algorithm definite and decreases their standard deviation, but also resolves the weakness of the hyper spherical search optimization Algorithm based on Chaos Theory including the speed of convergence and the weak performance in some benchmark functions. The simulation results on the standard benchmark functions show that the proposed Algorithm not only has faster convergence, but also acts as a more accurate search Algorithm to find the optimal solution in comparison to the standard hyper spherical search Algorithm, chaotic hyper sherical search Algorithm, and some other optimization Algorithms such as genetic, particle swarm, and harmony search Algorithm.

The intriguing characteristics of chaotic maps have prompted researchers to use these sequences in watermarking systems to good effect. In this paper we aim to use a tent map to encrypt the binary logo to achieve a like-noise signal.This approach makes extraction of the watermark signal by potential attacker very hard. Embedding locations are selected based on certain principles.Experimental results demonstrate that our proposed watermarking method is highly superior to other techniques reported in literature and readily achieves the desired robustness and security level.

2The current research was conducted with the aim of compiling and validating the educational package of the 10th grade religion and life lesson based on Choice Theory. The research method for the stage of designing and compiling the educational package was descriptive-analytical method mixed . The main themes of the training package and the time required for training were taken from written sources such as the model of G schools and internal and external research projects. To validate the package, while using the face validity method, the content validity method was used in a qualitative and quantitative way. It was used with a panel of 14 experts. 14 experts were considered as the statistical population, 10 of them were in the field of psychology, two were in the field of curriculum planning, and two were in the field of education. In addition, two relative coefficients of content validity and content validity index were used. The minimum and maximum relative content validity coefficients for each item of the package were calculated as 1 and 0.71, and the content validity index in most sessions reached the number (0.85) for all goals in the educational protocol. The results revealed that the educational package of the Religion and Life lesson based on Choice Theory for 10th grade students have good formal and content validity and has the necessary validity for educational applications and research use.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Stock market is one of the options available to invest in liquidity. Investors in this area used a variety of approaches to predict stock prices. But due to the nonlinear relationship between variables affecting stock prices, Artificial Neural Networks are one of the most suitable approaches for this work. These networks, through different search optimization Algorithms, try to identify the relationships between these variables. The higher the Algorithms used, the higher the efficiency of the Algorithms, the more accurate the identification of the relationships between the variables. In this paper, an attempt has been made to combine chaotic maps and colonial competition Algorithms with the reform movement angle to the colonial colonies so that we can deal with the possibility of being trapped in local optimum to reduce as much as possible. Therefore, using this approach, it is tried to predict the stock price of Iran Khodro Company. To evaluate the performance of the proposed approach to other conventional approaches of neural network education, three perspectives: the degree of accuracy of prediction, the amount of memory used and the time of execution were used. The results show that the proposed approach has a better performance than other approaches.

Optimization Algorithms inspired by nature as intelligent optimization methods with classical methods have demonstrated significant success. Some of these techniques are genetic Algorithms, inspired by biological evolution of humans and other creatures) ant colony optimization and simulated annealing method (inspired by the refrigeration process metals). The methods for solving optimization problems in many different areas such as determining the optimal course of their work, designing optimal control for industrial processes, solving industrial engineering major issues such as the optimal layout design for industrial units, problem solving, and queuing in the design of intelligent agents have been used. This paper introduces a new Algorithm for optimization, which is not a natural phenomenon, but a phenomenon inspired teaching-human. It is entitled Education System Algorithm (ESA). Results demonstrate this method is better than other method in this area.

Background and Objectives: This paper explores the realm of optimization by synergistically integrating two unique Metaheuristic Algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these Algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance.Methods: The study introduces a novel hybrid Metaheuristic Algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the Algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC 2005, CEC 2019, and 2022, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other Metaheuristic Algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO).Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by 42%, 55%, 74%, 71%, 48%, and 52%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed Algorithm demonstrate its superior performance when compared to several well-regarded Algorithms documented in the literature.Conclusion: In conclusion, WHOFWA, the hybrid Metaheuristic Algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark Algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of Metaheuristic Algorithms.

Various Algorithms have proposed during the last decade for solving different complex optimization problems. The meta-heuristic Algorithms have been highly noted among researchers. In this paper, a new Algorithm, known as the Buzzards Optimization Algorithm (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new optimization Algorithm. The Algorithm performance has been compared with newest and well-known meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known Algorithms.