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.

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.

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.

Various ALGORITHMs have proposed during the last decade for solving different complex OPTIMIZATION problems. The meta-heuristic ALGORITHMs have been highly noted among researchers. In this paper, a new ALGORITHM, known as the Buzzards OPTIMIZATION ALGORITHM (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new OPTIMIZATION ALGORITHM. The ALGORITHM performance has been compared with newest and well-known meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known ALGORITHMs.

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.

To enhance the performance of meta-heuristic ALGORITHMs, the development of new operators and the efficient combination of various OPTIMIZATION techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel OPTIMIZATION ALGORITHM called PGS (Particle Swarm OPTIMIZATION-GA-Sliding Surface). PGS combines the strengths of particle swarm OPTIMIZATION (PSO), genetic ALGORITHM (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world OPTIMIZATION problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO ALGORITHM using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm OPTIMIZATION ALGORITHM, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed ALGORITHM's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent OPTIMIZATION techniques. Furthermore, we successfully implement the PGS ALGORITHM in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.