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.

In the absence of satellite ephemeris data and inner geometry of satellite’ s sensor, utilization of Rational Function Models (RFMs) is one of the best approaches to georeferencing satellite images and extracting spatial information from them. However, since RFMs have high number of coefficients, then usually high number of control points is needed for their estimation. In the other hand, RFM terms are uninterpretable and all of them causes over-parametrization error which count as the most important weakness of the terrain-dependent RFMs. Utilization of optimization algorithms is one of the best approaches to eliminate these weaknesses. Therefore, various optimization algorithms have been used to discover the optimal composition of RFM’ s terms. Since the mechanism of these algorithms is different, the performance and feature characteristics of these algorithms differ in the discovery of the optimal composition train-dependent RFM’ s terms. But the existing differences not comprehensively analyzed. In this paper, in order to comprehensive assessment the abilities of genetic Optimization algorithm (GA), genetic Modified algorithm (GM), and a Modified Particle Swarm Optimization (PSO) in terms of accuracy, quickness, number of control points required, and reliability of results, are evaluated. These methods are evaluated using for different datasets including a GeoEye-1, an IKONOS-2, a SPOT-3-1A, and a SPOT-3-1B satellite images. In terms of accuracy achieved, difference between these methods was less than 0. 4 pixel. In terms of speed of evaluation of parameters, GM was 10 to 12 time more quickly in comparison with two other algorithms. In terms of control points required, degree of freedom of Modified PSO was 45. 25 percent and 27 percent more than GM and GA respectively, and finally in terms of reliability, the dispersion of RMSE obtained in 10 runs of three algorithms are relatively same. These results indicated that accuracy and reliability of all three methods are almost the same, speed of GM is higher and Modified PSO needs less control points to optimize terrain-dependent RFM.

One of the major purposes of optimization in civil engineering is to perform a suitable design for the structure. This goal has to fulfill technical criteria and contain the minimum economical costs. Building frames are of the most customary civil engineering structures. Therefore, optimization of these types of structures could be of a great concern from the economical viewpoints. One of the current obstacles in such optimization problems is the local convergence debility. Thus, using means of tackling this problem seems necessary. genetic algorithm which is one of the optimization methods inspired by nature, has overcome this problem. In order to solve such problems, genetic algorithm needs a multiple analyses of structures. Therefore, in this study attempts were made to introduce and embed new formulae into a newly developed program to handle new techniques for selection and mutation as genetic operations. As for the aspects of application, the introduced techniques were inspected and investigated in the optimization of some planar and special braced steel frames. The outcome through comparisons proclaimed a considerable decrease in numbers of analyses as well as significant increases in the speed of convergence. 

Cybersecurity has turned into a brutal and vicious environment due to the expansion of cyber-threats and cyberbullying. Distributed Denial of Service (DDoS) is a network menace that compromises victims’ resources promptly. Considering the significant role of optimization algorithms in the highly accurate and adaptive detection of network attacks, the present study has proposed Hybrid Modified Grasshopper Optimization algorithm and genetic algorithm (HMGOGA) to detect and prevent DDoS attacks. HMGOGA overcomes conventional GOA drawbacks like low convergence speed and getting stuck in local optimum. In this paper, the proposed algorithm is used to detect DDoS attacks through the combined nonlinear regression (NR)-sigmoid model simulation. In order to serve this purpose, initially, the most important features in the network packages are extracted using the Random Forest (RF) method. By removing 55 irrelevant features out of a total of 77, the selected ones play a key role in the proposed model’s performance. To affirm the efficiency, the high correlation of the selected features was measured with Decision Tree (DT). Subsequently, the HMGOGA is trained with benchmark cost functions and another proposed cost function that enabling it to detect malicious traffic properly. The usability of the proposed model is evaluated by comparing with two benchmark functions (Sphere and Ackley function). The experimental results have proved that HMGOGA based on NR-sigmoid outperforms other implemented models and conventional GOA methods with 99.90% and 99.34% train and test accuracy, respectively.

Reliability-based optimization of two and three dimensional frame structures is the subject of this study. For this purpose, a computer program was developed and tested over a number of examples for validation. Since similar studies have been made previously for trusses and reliably documented in the literature, optimization of such structures based on reliability analysis could therefore be confidently relied on, and thus, designing of such structures could be considered with less value for safety factors. This probabilistic optimization technique can well substitute that of the deterministic one where a considerable factor of safety and therefore, a heavy structure as always is a must. For this purpose, one may take into account the probabilistic behavior for load, yield stress, young modulus, etc, using parameters such as standard deviation and variance, through which safety remarks can be embedded into the design procedure by some mathematical relations, resulting to a probabilistic optimization technique. In this technique, one must first define the failure criterion, followed by the computation of safety zone (Z), reliability index (b) and lastly, the failure probability (Pf). In this paper, the applied load and the yield stress are considered probabilistic, while the violation of interior forces from the member ultimate strength is the failure criterion. For each of the interior axial, shear, bending and torsion reactions, the failure probability is calculated and the maximum value is constrained through optimization process. During the optimization process using genetic algorithm (GA), the failure probabilities are some boundary constraints and minimizing the weight of structure is the objective of the problem. The profiles of I-shaped cross-sections are selected from a data file. Finally, the probabilistic technique and deterministic one are investigated and compared applied to some structural problems.

The array factor (sidelobe level, SLL) of a linear array is optimized using Modified continuous genetic algorithms in this work. The amplitudes and phases of the currents as well as the separation of the antennas are all taken as variables to be controlled. The results of the design using Modified GA versions are compared with other methods. Two design problems were studied using several continuous Modified GA versions and the results are presented as several plots. As a final example, the design specifications for an array with 200 elements are given. The effectiveness and advantages of the proposed Modified GA versions are outlined.