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 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.

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.

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.

Many real-world issues have multiple conflicting objectives, and optimization of the contradictory objectives is very difficult. In the recent years, the Multi-objective Evolutionary Algorithms (MOEAs) have shown a great performance in order to optimize such problems. Thus the development of MOEAs will always lead to the advancement of science. The Non-dominated Sorting Genetic Algorithm II (NSGAII) is considered as one of the most used evolutionary Algorithms, and many MOEAs such as the Sequential Multi-Objective Algorithm (SEQ-MOGA) have emerged to resolve the NSGAII problems. SEQ-MOGA presents a new survival selection that arranges the individuals systematically, and the chromosomes can cover the entire Pareto Front region. In this work, the Archive Sequential Multi-Objective Algorithm (ASMOGA) is proposed in order to develop and improve SEQ-MOGA. ASMOGA uses the archive technique in order to save the history of the search procedure so that the maintenance of the diversity in the decision space is adequately satisfied. In order to demonstrate the performance of ASMOGA, it is used and compared with several state-of-the-art MOEAs for optimizing the benchmark functions and designing the I-Beam problem. The optimization results are evaluated by the performance metrics such as the hyper-volume, generational distance, spacing, and t-test (a statistical test). Based on the results obtained, the superiority of the proposed Algorithm is clearly identified.

In this paper, echo cancellation is done using Genetic Algorithm (GA). The Genetic Algorithm is implemented by two kinds of crossovers, heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and different types of crossovers which are all satisfactory. Reverse SNR is used as the fitness function. It can estimate an echo path with definite length of impulse response with an adaptive filter with desired length.Results show that the proposed combined GA-LMS method operates more satisfactory than simple GA in terms of the number of generations needed to achieve a particular amount of echo cancellation. Different tests show that GAs running with heuristic crossover converge faster than GAs with microbial crossover. Results are also compared with LMS Algorithm. Although LMS is faster, but its solutions are less precise and it diverges in some cases. But our proposed method always converges.