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.

Nowadays, with the development of digital technology, the role of computer applications is important in art, especially in fabric and cloth design. Note that in traditional methods, it is not possible to interact with the consumer until the end of the design process, and if the final design is not approved by them, all design steps must be repeated. Typical design software systems only work well for professionals and are difficult for non-professionals to work with; therefore, there is a need to use systems that can provide the interaction between the user and the system while maintaining design speed, which is essential in this field. In this research, a fabric design assistance system based on the Interactive genetic algorithm has been developed. To design the fabric, patterns and colors available in Qashqai kilims, as well as the type of pattern arrangement in kilims, were used. The designs produced by the system are displayed to the user to estimate the level of fitness. According to the user's evaluation, weaker designs are discarded, and stronger designs are improved by passing through the system again, and finally, the desired design is created. The results show that the use of the proposed system in the fabric design industry enables designers, buyers, and even fabric manufacturers to apply their taste in the fabric design process. As inferred from the users' point of view, reducing the design process time, reducing related costs, and achieving multiple designs in the shortest possible time are the advantages of this system.

The Unequal Area Facility Layout Problem (UA-FLP) made using different methods to measure the quantity used. The plant features UA-FLP enhance productivity and can reduce between “20%” to “50%” of total operating costs. In this regard, an Interactive genetic algorithm (IGA) is presented that allows the Decision Maker (DM) to interact with the algorithm. In this method, the DM is to find the best and most appropriate solution. DM to avoid overloading, population classified into clusters and each cluster represents only one element is evaluated directly by the DM. But the problem is that the DM process to achieve the best result must pass many generations and it causes high exhaustion DM. The algorithm presented in this paper will reduce the wear on the DM. A memory of the best solutions chosen by the DM is kept as a reference. An Interactive genetic algorithm is presented able to take advantage of the DM.

Purpose: To compare the results of the new strategy Swedish Interactive Thresholding algorithm (SITA) Faster to the results of SITA Standard in patients with glaucoma. Methods: This was a cross-sectional study of 49 patients with glaucoma and previous experience with standard automated perimetry. Two consecutive tests were performed in random order, one with SITA Standard and another one with SITA Faster, in the studied eye of each patient. Comparisons were made for test time, mean deviation (MD), visual field index (VFI), and number of depressed points in pattern deviation map and total deviation map for every level of significance. Results: The average test time was 56% shorter with SITA Faster (P < 0. 001). The intraclass correlation coefficient (ICC) for MD and VFI showed excellent agreement between both strategies, ICC = 0. 98 (95% confidence interval [CI]: 0. 96, 0. 99) and ICC = 0. 97 (95% CI: 0. 95, 0. 99), respectively. For the number of depressed points in total deviation map and pattern deviation map, ICC demonstrated good agreement with values between 0. 8 and 0. 95. Conclusions: Our study shows that SITA Faster is a shorter test with strong agreement with SITA Standard parameters. These results suggest that SITA Faster could replace SITA Standard for glaucoma diagnosis.

In this paper we investigate the performance of simulated annealing (SA) and genetic algorithm (GA) in preventive part replacement for minimum downtime maintenance planning. Therefore some evaluation criteria are explained in order to analyze the performance of the algorithms. So it can be decided which algorithm is more suitable to apply in preventive part replacement.

Natural camouflage, seamlessly blending animals with their surroundings, remains challenging for artificial counterparts. Some animals exhibit near-permanent camouflaging, a product of decades of genetic evolution with their environment. At the same time, chameleons and octopuses achieve the ideal desired instantaneous camouflaging, unlike the heuristic-based approach of the artificial camouflage design. To attain similar perfection seen in animals, an evolutionary approach to artificial camouflage pattern development is necessary. Developing nations, primarily adopting the camouflage patterns of their more developed counterparts, may find themselves at a disadvantage. This study proposes a genetic algorithm (GA)-based approach to aid designers in developing countries in crafting effective camouflage. By parameterising heuristic development as a procedural texturing problem and evolving colour assignments iteratively, this approach aims to emulate the evolutionary process seen in nature. Using the K-means algorithm, genes are initialised based on background image colours, exploring factorial combinations to achieve optimal camouflage. With a maximum of 100 iterations and Interactive feedback, the method addresses Nigeria's specific case and offers a faster development solution than developed nations' approaches. This evolutionary approach could revolutionise artificial camouflage development worldwide.

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.

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.