In recent years, the use of Meta-heuristic algorithms on various problem taken into consideration. Meta-heuristic algorithms in solving various problem, different performance show. An Meta-heuristic algorithm to solve a particular problem may have better performance than other algorithms and poorer performance have in other issue. In this study the performance of Meta-heuristic algorithms for a specific problem that explore cryption key Vigenere encryption algorithm will be examined. And Meta-heuristic different algorithms performance in terms of accuracy and speed of convergence of the results will be cryptanalyzed and the best algorithm is selected.

Conventional and classical optimization methods are not efficient enough to deal with complicated, NP-hard, high-dimensional, non-linear, and hybrid problems. In recent years, the application of meta-heuristic algorithms for such problems increased dramatically and it is widely used in various fields. These algorithms, in contrast to exact optimization methods, find the solutions which are very close to the global optimum solution as possible, in such a way that this solution satisfies the threshold constraint with an acceptable level. Most of the meta-heuristic algorithms are inspired by natural phenomena. In this research, a comprehensive review on meta-heuristic algorithms is presented to introduce a large number of them (i.e. about 110 algorithms). Moreover, this research provides a brief explanation along with the source of their inspiration for each algorithm. Also, these algorithms are categorized based on the type of algorithms (e.g. swarm-based, evolutionary, physics-based, and human-based), nature-inspired vs non-nature-inspired based, population-based vs single-solution based. Finally, we present a novel classification of meta-heuristic algorithms based on the country of origin.

Due to the increase in the volume of data and information in recent years, the issue of choosing the most appropriate feature for decision making has become very important. Classic attribute selection methods cannot work well on big data. Because feature selection is a complex problem, it seems appropriate to use meta-heuristic algorithms to solve this problem. In this paper, a new meta-heuristic algorithm inspired by nomadic migration to solve the feature selection problem is presented. This algorithm is named in honor of the Qashqai tribe. In this hybrid algorithm, the proportional function was designed based on the feature selection algorithm and based on minimizing the number of features and the amount of data error using neural network results. Then the Qashqai meta-heuristic algorithm was implemented on this fitness function and the results were compared with the well-known meta-heuristic algorithms of genetics and particle swarm. The results of the hypothesis test showed that the Qashqai optimization algorithm to solve the feature selection problem by the genetic algorithm and particle swarm is not defeated and in terms of convergence to the optimal solution works well.

The development of effective meta-heuristic algorithms is crucial for solving complex optimization problems. This paper introduces the Society Deciling Process (SDP), a novel socio-inspired meta-heuristic algorithm that simulates the social categorization into deciles based on metrics such as income, occupation, and education. The objective of this research is to introduce the SDP algorithm and evaluate its performance in terms of convergence speed and hit rate, comparing it with seven well-established meta-heuristic algorithms to highlight its potential in optimization tasks. The SDP algorithm's efficacy was evaluated using a comprehensive set of 14 general test functions, including benchmarks from the CEC 2019 and CEC 2022 competitions. The performance of SDP was compared against seven established meta-heuristic algorithms: Artificial Hummingbird Algorithm (AHA), Dwarf Mongoose Optimization algorithm (DMO), Reptile Search Algorithm (RSA), Snake Optimizer (SO), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Gazelle Optimization Algorithm (GOA). Statistical analysis was conducted using Friedman's rank and Wilcoxon signed-rank tests to assess the relative performance in terms of exploration, exploitation capabilities, and proximity to the optimum solution. The results demonstrated that the SDP algorithm outperforms its counterparts in terms of convergence speed and hit rate across the selected test functions. In statistical tests, SDP showed significantly better performance in exploration and exploitation, leading to a higher proximity to the optimum solution compared to the other algorithms. Furthermore, when applied to five complex engineering design problems, the SDP algorithm exhibited superior performance, outmatching the state-of-the-art algorithms in terms of effectiveness and efficiency. The Society Deciling Process (SDP) algorithm introduces a novel and effective approach to optimization, inspired by societal structure dynamics. Its superior performance in convergence speed, exploration and exploitation capabilities, and application to complex engineering problems establishes SDP as a promising meta-heuristic algorithm. This research not only demonstrates the potential of socio-inspired algorithms in optimization tasks but also opens avenues for further enhancements in meta-heuristic algorithm designs.

This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term containing the best solution found is added to the basic updating rule of the algorithm. The efficiency of the proposed NMA metaheuristic is illustrated by presenting five benchmark discrete truss optimization problems and comparing the results with literature. The numerical results demonstrate that the NMA is a robust and powerful meta-heuristic algorithm for dealing with the discrete sizing optimization problems of steel trusses.

This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model is developed for this problem, in which the basic truck scheduling problem in a cross-docking system is strongly considered as NP-hardness. Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm, resulted from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used. Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and time criteria. Results show that the hybrid PCICA algorithm performs better than the other algorithms in terms of the solution quality. Computational results indicate when the learning rate increases, its decreasing effect on processing time will growth and the objective function value is improved. Finally, the sensitivity analysis also indicates when the deterioration rate is reduced, its incremental effect is decreased over time.

In multi-label data, each instance is associated with a set of labels, instead of one label. Due to the increasing number of modern applications associated with multi-label data, multi-label classification has gained significant attention during recent years. As in single-label data, eliminating redundant and/or irrelevant features plays an important role in improving classification performance. In this paper, a hybrid method for multi-label feature selection problem based on combing filter and wrapper methods is proposed, where meta-heuristic algorithms are employed as the wrapper method. Since, the number of features in multi-label data is usually high, solely employing search algorithms for finding the optimal feature subsets has high computational burden, and is very possible to fail. Hence, irrelevant features are first detected and removed by a filter method. Then, salient features are found among the remained features by the help of meta-heuristic algorithms. A significant number of well-known meta-heuristic algorithms are employed as the wrapper method, in the proposed system. Experiments show that the proposed method obtains better classification results, compared to other algorithms.

The expansion of Internet technologies during the last decades has led to the dependence of user’s activities in cyberspace on services provided by computer networks. One of the most important services is Intrusion Detection System (IDS) which controls network traffic for detecting abnormal behavior as well as anomaly activities. The robustness of the IDS is considered as an essential issue in the networks. In this paper, a brand new model based on meta-heuristic algorithms is projected to detect abnormal packets. In order to develop a high-performance strategy, a benchmark dataset (NSL-KDD), high-accuracy feature selection method and four meta-heuristic algorithms are employed. The dataset consists of 150490 normal and abnormal packets which are captured from a military network connection, and 16 most important features are extracted among 41 features using wrapper feature selection method. The mentioned feature selection method uses the naïve-bayesian approach to evaluate feature subsets. After the feature selection process, four meta-heuristic algorithms are utilized to detect the anomalies in network. The parameters of the cost function (a combination of non-linear regression and sigmoid) are optimized using meta-heuristic algorithms. The experimental results show that the imperialist competitive algorithm (ICA) outperforms other implemented meta-heuristic algorithms in terms of accuracy.

The main purpose of this paper was to develop optimal models of reservoir operation in order to supply different downstream demands based on fuzzy logic and meta- heuristic algorithms. In this regard, firstly, the optimal values of the released water releases from Zayanderood reservoir during the operation period based on the objective functions and different constraints, were extracted using meta- heuristic algorithms including Genetic Algorithms, Particle Swarm Optimization, Differencial Evolution, Harmony Search and Charged System Search, considering the inflow and the downstream demands during this period. Then, with regard to the optimal values of the released water over a long period, using fuzzy logic based models such as ANFIS and a new combination of ANFIS and meta-heuristic algorithms, various types of operation models were developed. Finally, the performance of these models were evaluated with various performance evaluation criteria such as reliability, reversibility, vulnerability and sustainability.The results showed the proper performance of the new hybrid system in developing the reservoir operation model. According to the results, the sustainability values achieved by the new hybrid and classic systems were 0.30 and 0.18, respectively. Also, this hybrid system showed a good performance by supplying 95.99% of the total demands of Zayanderood reservoir system.

Investment decision, have always has been one of the most important issues. Investors are trying to achieve the highest efficiency and the least risk by selecting the best companies from Among a wide variety of companies considering to various financial indicators. Accordingly, today, there are many ways to analyze the data from this company. One of the ways is clustering that classification of the companies. However, the present study aimed to identify and distinguish successful from unsuccessful companies in Tehran Stock Exchange has been done using K-means clustering. Then this problem is solved using meta-heuristic algorithms. The results indicate that meta-heuristic algorithms compared with conventional methods, more efficient and have led to a global optimum. Also these results of Altman’s bankruptcy model were confirmed results of meta-heuristic algorithms.