Biomass is a crucial component of the carbon cycle,thus, accurate evaluation is essential to manage the forest and understand its role in climate change. Biomass estimation also supports the international reduced emission from deforestation and forest degradation (REDD), including cases such as deforestation reduction, sustainable management of forests, protection and enhancement of forest carbon reserves). Today, using remote sensing techniques with the help of field data has revolutionized the estimation of forest biomass. forest biomass estimation can be based on the processing of remote sensing data obtained from active sensors (for example, lidar and radar) and passive sensors (for example, optical sensors). In most previous studies, mainly vegetation indices (such as Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Soil-Adjusted Vegetation Index (SAVI)) have been used to estimate biomass. Using terrain data, the amount of biomass is estimated using allometric equations, and the required pre-processing is done on optical and radar images. The attributes obtained from the scattering matrix and the ratios of the components of the scattering matrix and the attributes obtained from H/α decomposition are extracted from the radar image and the attributes of vegetation, soil and water are extracted from the optical image. Results and discussion: In this study, in order to improve the accuracy of estimating the biomass of forest areas, the features extracted from the optical images of Sentinel-2 sensor and Sentinel-1 radar data as well as field data of Noor forest areas, Mazandaran province, whose forest cover type, Carpinus betulus and Quercus Castaneifolia and also includes rare species such as Populus Caspica Bornm trees. In this study, we used the genetic optimization method in four classes of mixed vegetation, natural forest, degraded forest, and forest reserves were studied. In this regard, multivariate linear regression and support vector regression have been used to model between ground data and radar and optical features. Genetic algorithm (GA) is one of the most common evolutionary algorithms. This method finds potential solutions to optimize problems at the right time, especially when the search space is very wide. Also, a genetic algorithm has been used during the modeling process using multivariate linear regression to select the optimal features extracted from radar and optical images. Evaluation of the results showed that the use of the multivariate regression method led to more accurate results than the support vector regression method in the study area. Also, evaluation of the results showed that using features selected by a genetic algorithm led to an accurate R2 of 0. 78, 0. 87, 0. 68, and 0. 79 for first to fourth vegetation classes, respectively. Therefore, the results showed that the efficiency of the genetic algorithm in feature selection for biomass estimation from satellite images using the multivariate regression method.

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.

Numerous algorithms have recently been invented with varying strengths and weaknesses, none of which is the best for all cases. Herein, a hybrid optimization method known as a PSOHHO optimization algorithm is presented. There are two methods for combining algorithms: parallel and sequential. We adopted the parallel method and optimized the algorithm's performance. We cover the weaknesses of one algorithm with the strengths of another algorithm using a new method of combination. In this method, using several formulas, the top populations are exchanged between the two algorithms, and a new population is created. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In this method, no changes are made to the algorithms. The main goal is to use existing algorithms. This method aims to attain the optimal solution in the shortest time possible. Two algorithms of particle swarm optimization (PSO) and Harris Hawks optimization (HHO) were used to present this method and five truss samples were considered to confirm the performance of this method. Based on the results, this method has rapid convergence speed and acceptable results compared to the other methods. It also yields better results than its basic algorithms.

The Fruit Fly optimization algorithm is an intelligent optimization algorithm. To improve accuracy, convergence speed, as well as jumping out of local optimum, a modified Fruit Fly optimization algorithm (MFFOV) is proposed in this paper. The proposed algorithm uses velocity in particle swarm optimization and improves smell based on dimension and random perturbations. As a result of testing ten benchmark functions, the convergence speed and accuracy are clearly improved in Modified Fruit Fly optimization (MFFOV) compared to algorithms of Fruit Fly optimization (FFO), Particle Swarm optimization (PSO), Artificial Bee Colony (ABC), Teaching-Learning-Based optimization (TLBO), Genetic algorithms (GA), Gravitational Search algorithms (GSA), Differential Evaluations (DEs) and Hunter–Prey optimizations (HPOs). A performance verification algorithm is also proposed and applied to two engineering problems. Test functions and engineering problems were successfully solved by the proposed algorithm.

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.