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 scheduling, from both theoretical and practical points of view, a set of machines in parallel is a setting that is important. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view, the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program is necessary because the members of the program are performed in a parallel fashion, and this performance is executed according to some precedence relationship. This paper shows the problem of allocating a number of non-identical tasks in a multi-processor or multicomputer system. The model assumes that the system consists of a number of identical processors, and only one task may be executed on a processor at a time. Moreover, all schedules and tasks are non-preemptive.

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.

The present study is an attempt to propose a mutation-based real-coded Genetic Algorithm (MBRCGA) for sizing and layout Optimization of planar and spatial truss structures. The Gaussian mutation operator is used to create the reproduction operators. An adaptive tournament selection mechanism in combination with adaptive Gaussian mutation operators are proposed to achieve an effective search in the design space. The standard deviation of design variables is used as a key factor in the adaptation of mutation operators. The reliability of the proposed Algorithm is investigated in typical sizing and layout Optimization problems with both discrete and continuous design variables. The numerical results clearly indicated the competitiveness of MBRCGA in comparison with previously presented methods in the literature.

The optimized shape of trusses are found by presenting a new selection and reproduction operator. The stress, buckling an displacement constraints, consistent with design code, are used. Design variables are discrete size variables (member areas) and continuous shape variables (nodal coardinates). The proposed methods, is fast and has a stable convergence. In addition, it results in an optimized structure with low weight.

This paper presents a novel Meta-Heuristic method for solving an extended Markowitz Mean–Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the method of VaR calculation its minimizing methodology differs. if we use Historical Simulation which is applied in this paper then the curve would be nonconvex.On the other hand the Mean-VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new Meta- Heuristic approach based on combined Ant Colony Optimization (ACO) method and Genetic Algorithm (GA). The computational results show that the proposed Hybrid Algorithm has the ability to optimized Mean-VaR portfolio for small portfolio.

This paper presents a novel Meta-Heuristic method for solving an extended Markowitz Mean Variance portfolio selection model. The extended model considers Value-at-Risk (VaR) as risk measure instead of Variance. Depending on the method of VaR calculation its minimizing methodology differs. If we use Historical Simulation which is applied in this paper then the curve would be non-convex.On the other hand the Mean VaR model here includes three sets of constraints: bounds on holdings, cardinality and minimum return which cause a Mixed Integer Quadratic Programming Problem. The first set of constraints guarantee that the amount invested (if any) in each asset is between its predetermined upper and lower bounds. The cardinality constraint ensures that the total number of assets selected in the portfolio’s equal to a predefined number.Because of above mentioned reasons, in this paper, we propose a new Meta Heuristic approach based on combined Ant Colony Optimization (ACO) method and Genetic Algorithm (GA). The computational results show that the proposed Hybrid Algorithm has the ability to optimized Mean VaR portfolio for small portfolio.

This paper investigates Optimization methods based on Genetic Algorithms (GAs) for spiral heat exchangers. The purpose of designing heat exchanger depends on its application and could be total cost, heat transfer coefficient or both of them. The current targeting methods identify optimum points from both economic and thermodynamic views and capture a trade-off between two objectives. Optimizations using single objective functions are performed in order to investigate parameter behavior in two different applications of SHEs. Also this work takes care of numerous geometric parameters in the presence of logical constraints. Multi-objective and weighted function Optimizations using Genetic Algorithm are developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Optimized heat transfer coefficient compared to its first value at basic design had a 13% increase and total cost in optimized case presents 50% reduction compared to the basic design. Also in trade-off cases, heat transfer coefficient and total cost have been improved up to 60% increment and 20% reduction respectively. Therefore, designing heat exchanger using presented optimal methods in this research are proposed as useful methods for designers, engineers and researchers.