National Standardized School Exam (USBN) is used to determine Student’s graduation. This research aims to determine the characteristics of items for Math USBN in SMP on grade 9. This kind of research ...is a descriptive-explorative quantitative research. The data collected is a USBN test instrument at SMP Negeri 3 Pati and participants' answers, which are collected by documentation. The USBN instrument was validated by experts and the characteristic items of USBN instrument were analyzed using the classical test theory approach. The question items of math USBN test at SMP Negeri 3 Pati is generally moderately good. Based on the classical theory approach, the result of the validity is 0.924. 56.7% of items are very valid. The reliability is 0.78 categorized as high reliability. Generally, Math USBN items are in the easy category with a percentage of 83.3%. The results of discrimination index indicate that in general, USBN items are in a moderate category with a percentage of 60%. The distraction effectiveness shows that USBN items are in the functional category with a percentage of 50%.
This study presents a new metaheuristic optimization algorithm named Tree Optimization Algorithm (TOA) for solving mathematical benchmark functions and engineering problems. This algorithm, which is ...inspired from the growth of trees, starts from a random initial population and improves their performance according to the growth pattern of trees. Indeed, the purpose of this new optimization method is to find the highest leaf of a tree by utilizing the position of the best leaf, and also replacing yellow dried leaves by new random fresh green ones. These strategies prevent the algorithm from the premature convergence and getting stuck in local minima. This modern optimization method is evaluated by solving several mathematical test functions and a real world constrained design problem. The obtained results are compared with those of some prominent evolutionary algorithms introduced in the literature. The numerical and simulation results verify the superiority of the TOA in terms of the solution accuracy and the convergence speed.
Artificial Immune System (AIS) is inspired by nature biological immune system. AIS algorithm has ability to improve the global searching during optimization. However, hypermutation of AIS itself ...cannot always guarantee a better solution for convergence and accuracy. Therefore Genetic Algorithm (GA) has been used efficiently in solving complex optimization problems. The capability of individual algorithm can makes the new algorithm techniques more efficiency by overcome the shortcomings and without losing their own advantages. This paper demonstrates a hybrid algorithm known as Transform of Artificial Immune System (Trans-AIS) by combining AIS and GA algorithm. There are three mathematical test function are used for comparison to achieve the minimum value which are Rastrigin's, DeJong's and Griewank's functions. In this paper, the simulation of the test function results by using AIS and Trans-AIS will compare with optimization results by other researchers. By comparing the results, it is observed that the performance of Trans-AIS is comparable (if not superior) to other researchers' algorithm.
This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), ...provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. The key idea of this research is applying negative selection which is a technique in AIS to reduce the number of initial chromosomes and increase strong fitness to a local search space. In addition, the author of this paper has also compared the differences between the minimum fitness values of the testing functions, five mathematical test functions were used to make comparisons. The results from GA, AIS, and PSO illustrated that the IGA produced good quality solutions and outperforms similar methods.
The paper is focusing on Particle Swarm Optimization (PSO) algorithm. Several variants of the PSO algorithm are studied. To evaluate their performances a software tool has been developed in Matlab ...environment. Three reference mathematical test functions have been used for this purpose. This paper represents a necessary step requested by the optimal power flow (OPF) computing approach. Currently the authors are focusing on developing a PSO based OPF computing algorithm. The most suitable PSO algorithm variant is provided based on the analyses within the current paper.
Nowadays, there is a huge interest regarding the use of artificial intelligence techniques. From the associated methods, the current work is focusing on particle swarm optimization (PSO) study. The ...authors aim to present a synthesis regarding the PSO applications within the power system field. Two issues are addressed within the paper. Firstly, the PSO parameter tuning using mathematical test functions. Secondly, the conclusions are applied in case of optimal power flow (OPF) computing for small scale test power systems. For both issues the methodologies are presented, software tools have been developed. The research work is going to be continued having as a goal to develop a PSO based software designed for transmission network expansion (in case of complex power systems).
This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual ...algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; firstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efficiency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To differentiate between the results in terms of achieving the minimum value for these functions, eight mathematical test functions are being used to make comparison.
Genetic algorithms. Power systems applications Solomonese, Florin; Barbulescu, Constantin; Kilyeni, Stefan ...
2013 6th International Conference on Human System Interactions (HSI),
2013-June
Conference Proceeding
Nowadays, there is a huge interest regarding the use of artificial intelligence techniques. From the associated methods, the current work is focusing on genetic algorithms (GA) study. The authors aim ...to present a synthesis regarding the GA applications within the power system field. Two issues are addressed within the paper. Firstly, the GA parameter tuning using mathematical test functions. Secondly, the conclusions are applied in case of optimal power flow (OPF) computing for small scale test power systems. For both issues the methodologies are presented, software tools have been developed. The research work is going to be continued having as a goal to develop a GA based software designed for transmission network expansion (in case of complex power systems).
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