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A two-stage framework for bat algorithm
Neural Computing and Applications, 2016Bat algorithm (BA) is a new approach designed by imitating bat’s behavior of searching and capturing preys. The existing results have demonstrated the effectiveness and efficiency in comparison with other heuristic algorithms such as genetic algorithms and particle swarm optimization.
Boyang Zhang +5 more
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A Multi-objective Binary Bat Algorithm
Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication, 2015Bat Algorithm (BA) is a recently proposed heuristic algorithm based on the echolocation behavior of bats. BA has proven to have better performance than other well-known algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). However, the research on using BA for binary and multi-objective optimization has just begun. In this paper
Mohamed Amine Laamari, Nadjet Kamel
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A New Bat Based Back-Propagation (BAT-BP) Algorithm
2014Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate.
Nazri Mohd Nawi +2 more
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Enhanced shuffled bat algorithm (EShBAT)
2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2016Bat Algorithm (BA) is a simple and effective global optimization algorithm which has been applied to a wide range of real-world optimisation problems. Various extensions to Bat algorithm have been proposed in the past; prominent amongst them being ShBAT. ShBAT is a hybrid between BA and Shuffled Frog Leaping Algorithm-SFLA; a memetic algorithm based on
Reshu Chaudhary, Hema Banati
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Cultural Bat Optimization Algorithm: An Improved Variant of Bat Algorithm
2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), 2022K. A. Amalo +2 more
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Batting order optimization by genetic algorithm
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, 2012Baseball has been widely studied in various ways, including math and statistics. In a baseball game, an optimized batting order helps the team achieves greater number of runs in a season. This paper introduces a method that combines a genetic algorithm with a statistical simulation to identify a non-optimal batting order.
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Study of Parameter Sensitivity on Bat Algorithm
2017Heuristics and metaheuristics are known to be sensitive to input parameters. Bat algorithm (BA), a recent optimization metaheuristic, has a great number of input parameters that need to be adjusted in order to increase the quality of the results. Despites the crescent number of works with BA in literature, to the best of our knowledge, there is no work
Iago Augusto de Carvalho +4 more
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