The multidimensional knapsack problem（MKP） is a famous NP-hard combinatorial optimization problem with strong engineering *** this paper,we propose an improved migrating birds optimization（IMBO） to solve the *** I...
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ISBN:
(纸本)9781509046584
The multidimensional knapsack problem（MKP） is a famous NP-hard combinatorial optimization problem with strong engineering *** this paper,we propose an improved migrating birds optimization（IMBO） to solve the *** IMBO,to guarantee the initial swarm with a certain level of quality and diversity,we generate some meaningful solutions while other individuals are constructed *** addition,considering the characteristics of MBO and MKP,an effective sharing scheme（NSS） is designed to deliver useful information to the following *** experiments are performed and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed IMBO for solving the MKP.
RSA encryption is one of the public-key methods that has been popular in last decade. Considering increment of security requirements, size of the keys has been larger. With key length growing, delay of exponentiation ...
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ISBN:
(纸本)9780769539270
RSA encryption is one of the public-key methods that has been popular in last decade. Considering increment of security requirements, size of the keys has been larger. With key length growing, delay of exponentiation computation has changed into major problem in selecting longer keys. The binary or in other words square-and-multiply method is the classical exponentiation technique that is used in RSA. In this paper a new algorithm of exponentiation in RSA is presented that works in parallel, needs fewer multiplications and so has less delay. Therefore this technique is more useful in larger key computations.
A Cooperated fruit fly optimization algorithm (CFOA) is proposed for knapsack problems. In CFOA, a group generating strategy is designed for generating the initial solution. A novel cooperation strategy is used to enh...
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ISBN:
(纸本)9781538635247
A Cooperated fruit fly optimization algorithm (CFOA) is proposed for knapsack problems. In CFOA, a group generating strategy is designed for generating the initial solution. A novel cooperation strategy is used to enhance the connection and communication between flies. A repair operator based on value-weight ratio of each item is employed to guarantee the feasibility of the solution and enhance the usage rate of the constraint. Extensive numerical experiments are conducted on some well-known benchmark instances and the results show that CFOA presents extreme fast convergence speed and accuracy.
Information about encryption and a dozen practical importance of it can be visualized in the last three decades due to the development in the field of computer science data resource and Internet sharing. Re-visiting t...
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The Whale Optimization algorithm (WOA) is a widely-used approach for problem-solving, but it has some inherent limitations such as poor exploration capabilities, susceptibility to local optima, and reduced solution ac...
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The Whale Optimization algorithm (WOA) is a widely-used approach for problem-solving, but it has some inherent limitations such as poor exploration capabilities, susceptibility to local optima, and reduced solution accuracy. To address these drawbacks, this study introduces a novel approach known as Horizontal Crossover and Co-operative-hunting-based WOA (HCCWOA). This enhanced algorithm incorporates a weight, co-operative learning techniques, and a horizontal crossover strategy into the WOA framework. The introduction of horizontal crossover bolsters the exploration capabilities of WOA, while the integration of co-operative learning techniques and an inertia weight enhances its exploitation abilities. Recognizing the significance of feature selection in this context, the proposed algorithm is applied in a wrapper mode with the K-Nearest Neighbor (KNN) classifier to select relevant features. The effectiveness of the HCCWOA is rigorously evaluated on twelve classical datasets sourced from the UCI repository. A comprehensive comparison is conducted with eight well-established meta-heuristic algorithms and five recent variations of WOA. Performance metrics, including maximum accuracy, minimum fitness, and minimum feature count, are considered in this comparative analysis. The simulation results affirm that the HCCWOA outperforms other algorithms in at least six out of the twelve datasets. This enhanced performance is further substantiated through statistical analyses, including Friedman's rank test, paired-sample Wilcoxon signed-rank test, two-way ANOVA test, T-test, and boxplot analysis. The combination of empirical results and statistical validation supports the superior effectiveness of the proposed HCCWOA approach, highlighting its ability to effectively explore feature spaces and select the most relevant characteristics for classification tasks.
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