The prey-predator algorithm is a metaheuristic algorithm inspired by the interaction between a predator and its prey. Initial solutions are put into three categories: the better performing solution as the best prey, t...
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The prey-predator algorithm is a metaheuristic algorithm inspired by the interaction between a predator and its prey. Initial solutions are put into three categories: the better performing solution as the best prey, the worst performing solution as a predator, and the rest as ordinary prey. The best prey totally focuses on exploiting its neighborhood while the predator explores the search space searching for a promising region in the search space. The ordinary prey will be affected by these two extreme search behaviors of exploration and exploitation. The algorithm has been tested and found to be effective in solving different problems arising from different disciplines including engineering, tourism, and management. Originally, the algorithm was designed to deal with continuous problems. However, many problems arising from real aspects are not continuous. Hence, in this paper the prey-predator algorithm will be extended to suit discrete problems. Examination timetabling is used to test the approach. The simulation results with appropriate statistical analysis show that the approach is as good as the cumulative best performance of results recorded in the literature for the selected benchmark problems.
In order to find out the optimal bidding strategies (BSs) of generating companies (GENCOs) in a competitive electricity market, it is necessary to solve a bilevel optimization problem. The first level of the problem r...
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In order to find out the optimal bidding strategies (BSs) of generating companies (GENCOs) in a competitive electricity market, it is necessary to solve a bilevel optimization problem. The first level of the problem relates to the GENCOs to strategically bid, and the second level solves the independent system operator's market clearing problem based on the maximization of social welfare. In order to model the incomplete information of participants in the market about cost coefficients of opponents and their forecast errors, a scenario-based programming framework is presented. In addition, a roulette wheel mechanism is used for scenario-generation process so that the forecast errors of coefficients are considered as random variables with known probability distribution functions. Then, each GENCO solves the bilevel optimization problem and maximizes its expected profit function. These bilevel problems are nonconvex, and the mathematical-based optimization technique is unable to handle the problem and obtain the nearly global optima. In order to resolve this issue, a novel prey-predator optimization algorithm is suggested to solve the first level of the bilevel problem and using the iterative method to find out the supply function equilibrium that is the optimal BSs of GENCOs. Applying to the IEEE 57- and 118-bus test systems with incomplete information studies, the performance of the proposed approach is successfully approved.
In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,*** proposed model is base...
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In this study,we have proposed an artificial neural network(ANN)model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17,*** proposed model is based on the existing data(training data)published in the Saudi Arabia Coronavirus disease(COVID-19)situation—*** prey-predator algorithm is employed for the *** perceptron neural network(MLPNN)is used in this *** improve the performance of MLPNN,we determined the parameters of MLPNN using the prey-predator algorithm(PPA).The proposed model is called the MLPNN–*** performance of the proposed model has been analyzed by the root mean squared error(RMSE)function,and correlation coefficient(R).Furthermore,we tested the proposed model using other existing data recorded in Saudi Arabia(testing data).It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi *** results reveal that the number of infected persons will increase in the coming days and become a minimum of *** number of recoveries will be 2000 to 4000 per day.
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radia...
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ISBN:
(纸本)9780735412415
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
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