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"Algorithmus"
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Analysis Effect of Tournament Selection on Genetic Algorithm Performance in Traveling Salesman Problem (TSP)
This study discusses effect of tournament selection on the way individuals compete on the performance of Genetic Algorithms so which one tournament selection is most suitable for the Traveling Salesman Problem (TSP). One algorithm in solving TSP is Genetic Algorithm, which has 3 (three) main operators, namely selection, crossover, and mutation. Selection is one of the main operators in the Genetic Algorithm, where select the best individuals who can survive (the shortest travel route). Tournament selection compares a number of individuals through a match to choose the best individual based on each fitness value, so that the winning individual (the individual going to the next generation) will be chosen. There is two way to compete in an individual in tournament selection is by tournament selection with replacement (TSWR) and without replacement (TSWOR). The final results of the study conducted TSWR gets the best fitness, even though the generation that gets the best fitness is reaching the maximum generation (takes longer to get the best fitness).
Journal Article
Analysis of music influence on order preference based on TOPSIS algorithm
Music, as a cultural treasure, is currently difficult to accurately measure its influence due to its special nature. In order to solve this problem, an evaluation model based on TOPSIS is proposed. The influence of music is defined as the combination of the number of followers and the number of followers of the same genre as the influencer, and then the TOPSIS method is used to quantitatively analyze the influence of music. By establishing the original data matrix of relevant music characteristics, and then forward and normalize the data matrix, calculate the distance between the data and the optimal solution and the worst solution, and obtain the quantitative score of the music influence value, a right Accurate measurement of music influence is realized.
Journal Article
Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem
This paper deals with a multiperiod multiobjective fuzzy portfolio selectiossn problem based on credibility theory. A credibilistic multiobjective mean-VaR model is formulated for the multiperiod portfolio selection problem, whereby the return is quantified by the credibilistic mean and the risk is measured by the credibilistic VaR. We also consider liquidity, cardinality, and upper and lower bound constraints to obtain a more realistic model. Furthermore, to solve the proposed model efficiently, an improved multiobjective bat algorithm termed IMBA is designed, in which three new strategies, i.e., the global best solution selection strategy, candidate solution generation strategy, and competitive learning strategy, are proposed to increase the convergence speed and improve the solution quality. Finally, comparative experiments are presented to show the applicability and superiority of the proposed approaches from two aspects. First, the designed IMBA is compared with seven typical algorithms, i.e., multiobjective particle swarm optimization, multiobjective artificial bee colony, multiobjective firefly algorithm, multiobjective differential evolution, multiobjective bat, the non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm 2 (SPEA2), on a number of benchmark test problems. Second, the applicability of the proposed model to practical applications of portfolio selection is given under different circumstances.
Journal Article
Problem of the P-Stockings applied to the location of facilities
One of the problems derived from the overcrowding of cities and industry is the location of optimal facilities, these types of problems suffer from the problem of dimensionality themselves. In the following article, the problem is formulated in a mathematical way and it is approached from a metaheuristic epistemology which is the only methodology for now to find local optimum, since these optimization problems are still open in the scientific literature because they are considered problems of nondeterministic polynomial time. The explored technique is that of genetic algorithms which uses the classical theory of evolution to formulate and solve optimization problems.
Journal Article
Task-Dependent Algorithm Aversion
Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task's perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms' perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.
Journal Article
The modified branch and bound algorithm and dotted board model for triangular shape items
Cutting Stock Problem (CSP) is a problem of cutting stocks into items with certain cutting rules. This study used the data where the rectangular stocks were cut into triangular shape items with various order sizes. This study used the Modified Branch and Bound Algorithm (MBBA) to determine the optimal cutting pattern then formulated it into a Dotted Board model. Based on the results, it showed that the MBBA produced three optimal cutting patterns, which used 6 times, 8 times, and 4 times respectively to fulfill the consumer demand. Then the cutting patterns were formulated into the Dotted Board model. Minimum trim loss of the three models are 1,774 cm 2, 1,720 cm 2dan 980 cm 2.
Journal Article
Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models
Today, stock market has important function and it can be a place as a measure of economic position. People can earn a lot of money and return by investing their money in the stock exchange market. But it is not easy because many factors should be considered. So, there are many ways to predict the movement of share price. The main goal of this article is to predict stock price indices using artificial neural network (ANN) and train it with some new metaheuristic algorithms such as social spider optimization (SSO) and bat algorithm (BA). We used some technical indicators as input variables. Then, we used genetic algorithms (GA) as a heuristic algorithm for feature selection and choosing the best and most related indicators. We used some loss functions such as mean absolute error (MAE) as error evaluation criteria. On the other hand, we used some time series models forecasting like ARMA and ARIMA for prediction of stock price. Finally, we compared the results with each other means ANN-Metaheuristic algorithms and time series models. The statistical population of research have five most important and international indices which were S&P500, DAX, FTSE100, Nasdaq and DJI.
Journal Article
Anti-intrusion strategy MANET inspired by the bacterial foraging optimization algorithm
Mobile ad hoc networks (MANET) are reflexivity, fast, versatile wireless networks that are particularly useful when traditional radio infrastructure is unavailable, such as during outdoor events, natural disasters, and military operations. Security may be the weakest link in a network due to its dynamic topology, which leaves it susceptible to eavesdropping, rerouting, and application modifications. More security problems with MANET exist than with its service Quality of Service (QoS). Therefore, intrusion detection, which controls the system to find further security issues, is strongly suggested. Keeping an eye out for intrusions is essential to forestall future attacks and beef up security. If a mobile node loses its power supply, it may be unable to continue forwarding packets, which depends on the structure's condition. The proposed study presents a security of trust and optimization that conserves energy methods for MANETs based on integrating the K means algorithm and Bacteria Foraging Optimization Algorithm (KBFOA). This work proposes a method for quickly and accurately determining which nodes should serve as Cluster Heads (CHs) by K means algorithm. This approach aims to choose a node with a high Sustainable Cell (SC) rate as the Header of a Cluster (HC). Each node will calculate its SC according to its unique factors, such as energy consumption, degree, remaining energy, mobility, and distance from the HC and base stations. The security of MANETs is improved by the inclusion of an algorithm in the proposed approach that detects and eliminates rogue nodes. This suggested approach will increase network stability and performance using a high-sustainable cluster head. The proposed approach will be achieved the highest reliability of clustering rate of 97%. The intended research will be accomplished Maximum security measures rate will be accomplished by 96%, the maximum rate of data transmission will be obtained of 96%, and greater efficient detection of malicious nodes ratio will be enhanced by 95%, lower energy consumption rate will be achieved by 0.09 m joules.
Journal Article
A review on genetic algorithm: past, present, and future
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.
Journal Article