Artificial -neural -network and genetic -algorithm for optimization of helical -blade -vertical -axis -wind -turbine

In this paper Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm are applied for solving factual power loss reduction problem. The regular actions of Atlantic blue marlin are emulated to design the Atlantic blue marlin (ABM) optimization algorithm and populace in the examination space is capriciously stimulated. Boops optimization (BO) algorithm is designed by imitating the actions of Boops. Boops possess the obliging stalking physiognomies. As a cluster they stalk the quarry by forming the key and subordinate clusters CSO is based on the drive and search behaviour of Chironex fleckeri. Obviously Chironex fleckeri will exploit the limbs to paralyze the pray by injecting the rancour. In real time world a General practitioner will treat the Sick person with various procedures and it has been imitated to model the Projected PS algorithm. In general people will be inoculated and then with respect to disorder and disease- medical treatment will be given by medicines. Inoculation, medicine and operation are the procedures have been considered as the phases of the projected PS algorithm. Atlantic blue marlin (ABM) optimization algorithm, Boops optimization (BO) algorithm, Chironex fleckeri Search Optimization (CSO) algorithm, General practitioner -Sick person (PS) optimization algorithm validated in IEEE 57, 300 systems and 220 KV network. Factual power loss lessening, power divergence restraining, and power constancy index amplification has been attained.

Every person would want maximum profit with as little as possible resources or capital. One example in everyday life is the problem of limited storage media but is required to get the maximum benefit. From this problem comes the term known as the knapsack problem. One of the problems with Knapsack is knapsack 0- 1, where knapsack 0-1 is a problem of storing goods where the item will be completely inserted or not at all. Completion of knapsack 0-1 problems can be helped using a metaheuristic algorithm. Metaheuristic algorithms include the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization (MBO) algorithm. This study aims to determine the resolution of knapsack 0-1 problems using the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization (MBO) algorithm and compare the optimal solutions obtained. This research method is divided into three main parts. First take data that includes the name of the item, the purchase price, the selling price and the weight of each item. The second is applying the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization algorithm (MBO) on 0-1 knapsack problems. The third program is made to facilitate the calculation of data with the help of Matlab R2015b software. The results of this study found that both algorithms both reached the optimal solution, but the convergence and running time obtained were different. The Migrating Birds Optimization (MBO) algorithm is faster converging than the Penguins Search Optimization (PeSOA) algorithm to get the optimal solution. And also the Migrating Birds Optimization (MBO) algorithm has better running time than the Penguins Search Optimization (PeSOA) algorithm to achieve maximum iteration.
 Keywords: Whale optimization algorithm, multi knapsack 0-1 problem with multiple constraints.

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.

With the rapid development of radio technology and its widespread application in the military field, the electromagnetic environment in which radar communication operates is becoming increasingly complex. Among them, human radio interference makes radar countermeasures increasingly fierce. This requires radar systems to have strong capabilities in resisting electronic interference, anti-radiation missiles, and radar detection. However, array antennas are one of the effective means to solve these problems. In recent years, array antennas have been extensively utilized in various fields, including radar, sonar, and wireless communication. Many evolutionary algorithms have been employed to optimize the size and phase of array elements, as well as adjust the spacing between them, to achieve the desired antenna pattern. The main objective is to enhance useful signals while suppressing interference signals. In this paper, we introduce the dandelion optimization (DO) algorithm, a newly developed swarm intelligence optimization algorithm that simulates the growth and reproduction of natural dandelions. To address the issues of low precision and slow convergence of the DO algorithm, we propose an improved version called the chaos exchange nonlinear dandelion optimization (CENDO) algorithm. The CENDO algorithm aims to optimize the spacing of antenna array elements in order to achieve a low sidelobe level (SLL) and deep nulls antenna pattern. In order to test the performance of the CENDO algorithm in solving the problem of comprehensive optimization of non-equidistant antenna array patterns, five experimental simulation examples are conducted. In Experiment Simulation Example 1, Experiment Simulation Example 2, and Experiment Simulation Example 3, the optimization objective is to reduce the SLL of non-equidistant arrays. The CENDO algorithm is compared with DO, particle swarm optimization (PSO), the quadratic penalty function method (QPM), based on hybrid particle swarm optimization and the gravity search algorithm (PSOGSA), the whale optimization algorithm (WOA), the grasshopper optimization algorithm (GOA), the sparrow search algorithm (SSA), the multi-objective sparrow search optimization algorithm (MSSA), the runner-root algorithm (RRA), and the cat swarm optimization (CSO) algorithms. In the three examples above, the SLLs obtained using the CENDO algorithm optimization are all the lowest. The above three examples all demonstrate that the improved CENDO algorithm performs better in reducing the SLL of non-equidistant antenna arrays. In Experiment Simulation Example 4 and In Experiment Simulation Example 5, the optimization objective is to reduce the SLL of a non-uniform array and generate some deep nulls in a specified direction. The CENDO algorithm is compared with the DO algorithm, PSO algorithm, CSO algorithm, pelican optimization algorithm (POA), and grey wolf optimizer (GWO) algorithm. In the two examples above, optimizing the antenna array using the CENDO algorithm not only results in the lowest SLL but also in the deepest zeros. The above examples both demonstrate that the improved CENDO algorithm has better optimization performance in simultaneously reducing the SLL of non-equidistant antenna arrays and reducing the null depth problem. In summary, the simulation results of five experiments show that the CENDO algorithm has better optimization ability in the comprehensive optimization problem of non-equidistant antenna array patterns than all the algorithms compared above. Therefore, it can be regarded as a strong candidate to solve problems in the field of electromagnetism.

Named Data Networking (NDN) intrinsically supports caching features in the network. This feature offers the potential to transmit content segments consisting of content requested from producers on the network. However, in NDN itself, there are a lot of caching techniques that are underutilized because of the complexity of the algorithm creation. There are several caching techniques based on a replacement algorithm, including Optimal algorithm, which focuses more on content that will not be used in the near future to store content in the content store. But Optimal algorithm currently has a weakness. It cannot combine the most recently accessed and most accessed content when used together, Optimal Modifications algorithm are made to combine the content that will be accessed and most accessed content in the decision to replace files so that Optimal modification algorithm can improve optimal performance. In this research, the optimal modification algorithm is proposed. Optimal algorithm performance and optimal modification algorithm are compared. The simulation results show that the optimal modification algorithm is feasible to improve optimal algorithm performance. When the number of number content interest frequency increased, the hit ratio increased by 0,26% and 0,45% in the network. In the case of Increasing the frequency of Interest packet, the packet drop parameters is not affected. Meanwhile, for the Hop Count and delay, the difference is not significant.

Optimization algorithms have been proved to be good solutions for many practical applications. They were mainly inspired by natural evolutions. However, they are still faced to some problems such as trapping in local minimums, having low speed of convergence, and also having high order of complexity for implementation. In this paper, we introduce a new optimization algorithm, we called it Stem Cells Algorithm (SCA), which is based on behavior of stem cells in reproducing themselves. SCA has high speed of convergence, low level of complexity with easy implementation process. It also avoid the local minimums in an intelligent manner. The comparative results on a series of benchmark functions using the proposed algorithm related to other well-known optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and artificial bee colony (ABC) algorithm demonstrate the superior performance of the new optimization algorithm.

<p class="Author">This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 & practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.</p>

We apply the enhanced Equus Ferus Przewalskii optimization (EEPO) algorithm and the advanced Osteolaemus search optimization (AOSO) algorithm to solve true power loss reduction problems. Equus Ferus Przewalskii tends to pursue and run in its environment. As a result, the adult Equus Ferus Przewalskii and steeds adopt an arbitrary course. At that juncture, a vibrant inertia weight approach is presented to the oasis, and the results will be valuable to poise the exploration and exploitation. The Equus Ferus Przewalskii optimization algorithm is combined with the Anarchias seychellensis and Peacock hind’s teamwork-based optimization algorithm to improve the exploration ability of the process. The Osteolaemus search optimization algorithm imitates the two key phases of Osteolaemus behavior-ringing and stalking. Osteolaemus have flawless nocturnal vision and are primarily nocturnal stalkers. Osteolaemus employ the paleness of victim animals for their sustenance. Osteolaemus are ensnaring slayers, searching for nearby fish or terrestrial animals before proceeding to their next meal. Osteolaemus can track prey over short distances, even out of aquatic conditions. Osteolaemus have double passages in the course of the ringing; tall marching and tummy marching. The osteolaemus search optimization algorithm has assimilated the advanced features of the cryptoprocta search optimization algorithm. This assimilation will upgrade the exploitation competence of the process substantially. The Enhanced Equus Ferus Przewalskii optimization (EEPO) algorithm and the advanced Osteolaemus search optimization (AOSO) algorithm have been tested successfully on 7 standard functions, as well as the IEEE 30, 57, and 118 bus systems, and the Grid 220 kV system.

Based on the analysis of classical particle swarm optimization (PSO) algorithm, we adopted Sun’s theory that has the behavior of quantum particle swarm optimization (QPSO) algorithm, by analyzing the algorithm natural parallelism and combined with parallel computer high-speed parallelism, we put forward a new parallel with the behavior of quantum particle swarm optimization (PQPSO) algorithm. On this basis, introduced the island model, relative to the fine-grained has two quantum behavior of particle swarm,m optimization algorithm, the proposed two kinds of coarse-grained parallel based on multiple populations has the behavior of quantum particle swarm optimization (QPSO) algorithm. Finally under the environment of MPI parallel machine using benchmark functions to do the numerical test, and a comparative analysis with other optimization algorithms. Results show that based on the global optimal value is superior to the exchange of data based on local optimum values of exchange, but in the comparison of time is just the opposite.

This paper proposes Improved Brain Storm Optimization (IBSO) algorithm is used for solving reactive power problem. predictably, optimization algorithm stimulated by human being inspired problem-solving procedure should be highly developed than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, a new Improved brain storm optimization algorithm defined, which was stimulated by the human brainstorming course of action. In the projected Improved Brain Storm Optimization (IBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process & exchange of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Improved Brain Storm Optimization (IBSO) algorithm, has been tested standard IEEE 30 bus test system and compared to other standard reported algorithms. Simulation results show that Improved Brain Storm Optimization (IBSO) algorithm is superior to other algorithms in reducing the real power loss.

There are many optimization algorithms, most of them with many parameters. When you know which family of problems you face, you would like to design the optimization algorithm which is the best for this family (e.g., on average against a given distribution of probability on this family of optimization algorithms). This chapter is devoted to this framework: we assume that we know a probability distribution, from which the fitness function is drawn, and we look for the optimal optimization algorithm. This can be based (i) on experimentations, i.e. tuning the parameters on a set of problems, (ii) on mathematical approaches automatically building an optimization algorithm from a probability distribution on fitness functions (reinforcement learning approaches), or (iii) some simplified versions of the latter, with more reasonable computational cost (Gaussian processes for optimization).KeywordsFitness FunctionGaussian ProcessMarkov Decision ProcessSampling CriterionLinear ConvergenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Network-wide sewer odour and corrosion management by model predictive control

Large number of optimization algorithms necessarily raises the question about the best optimization algorithm. There seems to be no unique answer. It is logical that if an optimal optimization algorithm exists, then all other optimization algorithms would be superfluous. It is clear that the universal optimization algorithm, that can solve all problems occurring in practice, does not exist. All the optimization algorithms presently known can only be used without restriction in particular areas of application. According to the nature of a particular problem one or another optimization algorithm offers a more successful solution.

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algorithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assignment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate association cost; hence, much of the procedure time is saved. In the 2-stage association algorithm, a large number of false location points are eliminated from candidate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.

Gravitational search algorithm (GSA) is an optimization algorithm inspired from Newton's law of gravitation. Moth flame optimization (MFO) is another optimization algorithm, motivated by the locomotion of moths around a light source. Both of these algorithms have tried to model the search agents and altered properties like mass, gravitational constant, fitness, location, etc. in order to find the most optimal value. Optimization algorithms usually solve only a class of problems and therefore the search for a faster and more comprehensive algorithm is always on. By hybridizing MFO and GSA, the performance is expected to improve across various measures. This paper presents a hybrid optimization algorithm by using concepts of moth flame optimization and gravitational search algorithm and applies this hybrid algorithm to image segmentation. An optimized K-means algorithm and an optimized thresholding algorithm have been proposed. The results of the segmentation are then used to classify apples into different classes.