A Hybrid Optimization Technique Using Exchange Market and Genetic Algorithms

Utilization of Exchange Market Algorithm (EMA) for selective harmonic elimination (SHE) in cascaded multilevel inverters (CMIs) is investigated in this paper. The nonlinear SHE equations are derived for a 7-level CMI based on the Fourier expansion of the output voltage waveform. A cost function is constructed according to the SHE equations and optimization is performed with EMA. In order to compare the results, the SHE equations are also solved with Genetic Algorithm (GA) with similar parameters as used for EMA. Optimization results prove that EMA is superior to GA in terms of convergence rate, and achieving exact global minima. For verifying the optimization results, simulations are performed on a 7-level CMI for different modulation indices.

Medical image segmentation using exchange market algorithm

The large-scale integration of grid-scale energy storage systems (ESSs) motivates the development of techniques for determining the optimal ratings and locations of storage devices. To achieve the best optimal results, exchange market algorithm (EMA) is used. EMA is a new meta-heuristic method for solving the optimizing problems. This optimization algorithm is inspired by the procedure of trading the shares on the stock market. Evaluation of how the stocks are traded on the stock market by elites has formed this algorithm. This paper proposes a method for identifying the sites where ESSs should be located to perform most effectively. It has been tested on a standard 33 bus radial distribution system. A method for determining the optimal operation of ESSs to obtain the least power loss is proposed. The main purpose of the operation strategy is to minimize the peak generation in which the power plants generate with the least oscillation. To validate the effectiveness of this method different scenarios are investigated. In order to proof this optimization method, several comparisons have been done. Finally, the storages optimal charge and discharge rate, location, and power loss improvement are presented. The results show the ability of the EMA in finding the global optimum point of the storage and their hourly charging rate.

This paper presents an application of exchange market algorithm (EMA) in solving multi-objective optimisation problems of power systems. This optimisation algorithm is based on the activities of shareholders to maximise their profit in the exchange market. The uniqueness of this algorithm lies in the fact that, it enjoys double exploitation and exploration property unlike several other algorithms. In order to investigate its search capability, the EMA is utilised to solve power systems active and reactive related objectives simultaneously in presence of several nonlinear constraints. Both optimum generation and VAr planning problems are formulated as conventional optimal power flow (OPF) problem. Fuel cost (active related objective), transmission line loss and total voltage deviation (reactive related objectives) are taken as different objective functions. The multi-objective optimisation problem is performed through weighted sum approach. Both fuzzy and equal weight approach is utilised to declare the compromised solution. Programs are developed on MATLAB and simulations are performed on Standard IEEE-30 and IEEE-57 bus systems. The search capability of EMA in solving the multi-objective power system problems are compared with PSO based solutions.

Optimal placement and sizing of distributed generation (DG) is one of the important issues in the radial distribution system, playing a vital role in reduction power losses and total cost of the system. Optimal placement and sizing of DG have many advantages for the radial networks, which some of the important them are the reliability and voltage stability improvement, power loss and emission reduction. In this paper, Exchange market algorithm (EMA) as a new heuristic algorithm is used for solving multiple DG placement and sizing problem in the radial distribution system. EMA is consisted of two powerful searcher operators which are used to create and organize the random numbers of the initial population and it can be employed to find the optimum point in the optimization problems. In order to evaluate the purposes of this study, EMA is applied on three test systems, including 33, 69 bus IEEE test system and 94 bus Portuguese radial distribution system. The obtained numerical results of the optimization procedure are compared with recent studies in this regards and the resulting analyses indicated that the power losses in the radial network reduced to the minimum amount of ownership. The results also prove the effectiveness of the EMA as one of the powerful optimization tools in solving DG placement problem.

A rank-based constraint handling technique for engineering design optimization problems solved by genetic algorithms

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. They are considered as a search-based technique based on the principles of Genetics and Natural Selection used in computing to find an exact or approximate solution for optimization and search problems. Genetic algorithms are also termed as heuristic search algorithms that belong to the larger part of evolutionary algorithms. They are inspired by evolutionary biology such as selection, cross over, and inheritance mutation. These algorithms provide a technique for the program to automatically improve the class of their parameters and generate high-quality solutions for optimization problems and search problems. This paper is an introduction of the Genetic algorithm approach and briefly describes some of the most interesting research or applications which are used in real-world projects that enable readers to implement and experiment with Genetic Algorithm on their own.

Evolutionary algorithms, simulated annealing and tabu search: a comparative study

Because of the complexity of some optimization problems, evolutionary and meta-heuristic algorithms are sometimes more applicable than the traditional optimization methods. Some difficulties in solving design-operation problems in the field of engineering are due to the multi-modality of the solution region of these problems. Since the design variables usually are specified as discrete variables and other continuous decision variables have to be set according to the range of the discrete ones, the possibility of trapping the final solution into some local optimum increases. In such cases, the capability of both traditional and evolutionary algorithms decreases. Thus, the development of a strategy to overcome this problem is the subject of this paper. For water utilities, one of the greatest potential areas for energy cost-savings is the effective scheduling of daily pump operations. Optimum design operation of pumping stations is a potential problem in this area that performs a wide background of solutions to this problem with different methods. Computation in all methods is driven by an objective function that includes operating and capital costs subject to various performances and hydraulic constraints. This paper achieves the optimal control and operation of an irrigation pumping station system by one of the latest tools used in optimization problems, which is the honey-bees mating optimization (HBMO) algorithm and is tested with a practical design. The HBMO algorithm with dynamic penalty function is presented and compared with two other well-known optimization tools which are the Lagrange multipliers (LM) method and genetic algorithms (GA) as well as with the previous results of the HBMO algorithm with constant penalty function for the same problem. The LM, GA and HBMO approaches simultaneously determine the least total annual cost of the pumping station and its operation. The solution includes the selection of pump type, capacity and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. In this paper, the HBMO algorithm is applied and the dynamic penalty function is tested to demonstrate the efficiency of this combination simultaneously. The results are very promising and prove the ability of combining the dynamic penalty function with the HBMO algorithm for solving combinatorial design–operation optimization problems. Application of all these models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and capability of the dynamic penalty function in the HBMO algorithm for solving complex problems of this type.

Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

This paper presents a comparative performance assessment for loss minimization of vector controlled induction motor (IM) drive based on two different efficient optimization algorithms, namely Genetic Algorithm (GA) and Golden Search (GS). Here the features of GA and GS both for estimation and recalculation of optimized flux component of current ( ), have been utilized for a better optimal efficiency operation of the IM drive. In this context, a comparative performance of IM drive based on GA and GS optimization algorithms is carried out. Both the GA and GS based algorithms greatly improve the efficiency by reducing the core loss of the drive system. Moreover, both the approaches have no effect on parameter variation and also need no additional hardware for hardware implementation. However, GA based loss minimization scheme proves its edge over GS based scheme for the IM drive. The simulation results for various speed patterns and operating conditions are presented here. Stability study of the whole drive system is also carried out utilizing both the optimizing schemes. The performance of the proposed drive is validated experimentally on dSPACE-1104 based laboratory prototype.

ABSTRACTOne of the complex and time‐consuming stages of land consolidation, where both parcel geometry in rural lands and the ownership structure are changed, is land reallocation. Land reallocation consists of land redistribution and land partitioning stages. Land redistribution aims at careful location of the parcels whose ownership statuses have been changed into land blocks constructed through taking many parameters into account. The focus of this study is the optimization of the land redistribution stage. The performance of many optimization algorithms such as genetic algorithms (GA), Artificial Bee Colony (ABC) Algorithm, and Migrating Birds Optimization (MBO) Algorithm has been investigated for the solution of the land redistribution problem. In this study, we apply the Clonal Selection Algorithm (CSA) to the land redistribution problem for the first time, offering a novel approach to improving allocation efficiency. CSA has been applied to the land redistribution problem, which considers various redistribution parameters. The study area covers 1551.1 ha; there are 351 cadastral parcels and 143 blocks where the parcel areas will be redistributed. So as to test the validity of the application results, the outcomes of CSA were compared with those results of Genetic Algorithms (GA), a biological‐based optimization method previously used to solve the problem. According to the results obtained for GA, the best fitness value was achieved (523.4672), and the placement rate based on the most successful preferences was (67.41294). According to the results obtained for CSA, the best fitness value was achieved (296.4551), and the placement rate based on the most successful preferences was (66.54229). The primary goal of this process was to find the lowest fitness values. In addition, the best suitability solutions found with GA and CSA were compared according to the block fullness rates. The deviations in block occupancy rates obtained by GA are larger than the deviations obtained by CSA.

This paper studies natural frequencies and mode shapes of a spinning disk/spindle assembly consisting of multiple elastic circular plates mounted on a rigid spindle that undergoes infinitesimal rigid-body translation and rotation. Through use of Lagrangian mechanics, linearized equations of motion are derived in terms of Euler angles, rigid-body translation, and elastic vibration modes of each disk. Compared with a single rotating disk whose spindle is fixed in space, the free vibration of multiple disks with rigid-body motion is significantly different in the following ways. First of all, lateral translation of the spindle, rigid-body rotation (or rocking) of the spindle, and one-nodal diameter modes of each disk are coupled together. When all the disks (say N disks) are identical, the coupled disk/spindle vibration splits into N − 1 groups of “balanced modes” and a group of “unbalanced modes.” For each group of the balanced modes, two adjacent disks vibrate entirely out of phase, while other disks undergo no deformation. Because the out-of-phase vibration does not change the angular momentum, the natural frequencies of the balanced modes are identical to those of the one-nodal-diameter modes of each disk. For the group of the unbalanced modes, all disks undergo the same out-of-plane vibration resulting in a change of angular momentum and a steady precession of the spindle. As a result, the frequencies of the unbalanced modes are significantly lower than those of one-nodal-diameter modes of each disk. Secondly, axial translation of the spindle and the axisymmetric modes of each disk are couple together. Similarly, the coupled motion split into N − 1 groups of “balanced modes” and one group of “unbalanced modes,” where the frequencies of the balanced and unbalanced modes are identical to and smaller than those of the axisymmetric modes of each disk, respectively. Thirdly, the rigid-body motion of the spindle does not affect disk vibration modes with two or more nodal diameters. Response of those modes can be determined through the classical vibration analysis of rotating disks. Moreover, vibration response of the disk/spindle assembly from a ground-based observer is derived. Finally, a calibrated experiment is conducted to validate the theoretical predictions.

In this study, an artificial intelligence technique is proposed to be implemented in the parameter tuning of a PVD process. Due to its previous adaptation in similar optimization problems, genetic algorithm (GA) is selected to optimize the parameter tuning of the RF magnetron sputtering process. The most optimized parameter combination obtained from GA’s optimization result is expected to produce the desirable zinc oxide (ZnO) thin film from the sputtering process. The parameters involved in this study were RF power, deposition time and substrate temperature. The algorithm was tested to optimize the 25 datasets of parameter combinations. The results from the computational experiment were then compared with the actual result from the laboratory experiment. Based on the comparison, GA had shown that the algorithm was reliable to optimize the parameter combination before the parameter tuning could be done to the RF magnetron sputtering machine. In order to verify the result of GA, the algorithm was also been compared to other well known optimization algorithms, which were, particle swarm optimization (PSO) and gravitational search algorithm (GSA). The results had shown that GA was reliable in solving this RF magnetron sputtering process parameter tuning problem. GA had shown better accuracy in the optimization based on the fitness evaluation.