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.

Over the past few decades great efforts were made to solve uncertain hybrid optimization problems. The n-Queen problem is one of such problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, parallel Genetic Algorithms are proposed to solve the n-Queen problem. Parallelizing Island Genetic Algorithm and Cellular Genetic Algorithm was implemented and run. The results show that these Algorithms have the ability to find related solutions to this problem. The Algorithms are not only faster but also they lead to better performance even without the use of parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial Genetic Algorithms in order to measure the performance of the proposed method. The experimental results show that the Algorithm has high efficiency for large-size problems in comparison with Genetic Algorithms, and in some cases it can achieve super linear speedup. The proposed method in the present study can be easily developed to solve other optimization problems.

This study aimed to estimate microsatellite based Genetic diversity in two lines (the selected RIRS and control line RIRC) of Rhode Island Red (RIR) chicken. Genomic DNA of 24 randomly selected birds maintained at Central Avian Research Institute (India) and 24 microsatellite markers were used. Microsatellite alleles were determined on 6% urea-PAGE, recorded using GelDoc system and the samples were genotyped. Nei’s heterozygosity and Botstein’s polymorphic information content (PIC) at each microsatellite locus were estimated. Wright’s fixation indices and gene flow were estimated using POPGENE software. All the microsatellite loci were polymorphic and the estimated PIC ranged from 0.3648 (MCW0059) to 0.7819 (ADL0267) in RIRS and from 0.2392 (MCW0059) to 0.8620 (ADL0136) in RIRC. Most of the loci were highly informative (PIC>0.50) in the both lines, except for five loci in RIRS and six loci in RIRC line. Nei’s heterozygosity per locus ranged from 0.4800 (MCW0059) to 0.8056 (ADL0267) in RIRS and from 0.2778 (MCW0059) to 0.875 (ADL0136) in RIRC. Out of 24 loci, 15 (62.5%) in RIRS and 14 loci (58.33%) in RIRC revealed moderate to high negative FIS index indicating heterozygote excess for these loci in corresponding lines, but the rest revealed positive FIS indicating heterozygosity deficiency. A mean FIS across the both lines indicated overall 10.77% heterozygosity deficit and a mean FIT indicated 17.19% inbreeding co-efficient favoring homozygosity over the two lines. The mean FST indicated that 10.18% of the microsatellite variation between the two lines was due to their Genetic difference.

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.