Monarch butterfly optimization (MBO) algorithm is a class of swarm intelligence metaheuristic algorithm inspired by the migration behavior of monarch butterflies. Through the migration operation and butterfly adjusting operation, individuals in MBO are updated. MBO can outperform many state-of-the-art ...
Section 5 gives the information about the Epsilon-fuzzy dominance-based Multi-Objective Improved Monarch Butterfly Optimization algorithm. Section 6 mentioned the usage of the simulation environment and the performance evaluation of the proposed approach. Finally, the last section contributes to the ...
THEORY AND APPLICATIONS OF SOFT COMPUTING METHODSMonarch butterfly optimizationGai-Ge Wang 1,2,3 • Suash Deb 4 • Zhihua Cui 5Received: 27 February 2015/Accepted: 5 May 2015? The Natural Computing Applications Forum 2015Abstract In nature, the eastern North American monarchpopulation is know...
Wang et al. proposed a new swarm intelligence-based metaheuristic algorithm, called monarch butterfly optimization (MBO), for addressing various global optimization tasks. The effectiveness of MBO was verified by benchmark evaluation on an array of unimodal and multimodal test functions in comparison...
Monarch Butterfly OptimizationClassificationK-nearest neighborFeature selectionWrapper approachClassification remains as a most significant area in data mining. Probabilistic Neural Network (PNN) is repeatedly used for classification problems. The main aims of this paper are to fine-tune the neural networks...
monarch butterfly optimization algorithmnormal cloud modelconvex lens imagingadaptive adjustment ratepressure vessel designwelded beam designIn recent years, a large number of nonconvex, highly nonlinear, multimodal, and multivariable complex optimization problems have emerged in scientific and engineering ...
butterfly adjusting operator (BAR)valve-point loading (VPL)ramp rate limitevolutionary algorithmsThis paper provides a computational methodology based on monarch butterfly optimization (MBO) to find a solution to the problem of cost-based unit commitment (CBUC). The binary variables of unit ...
Monarch butterfly optimization (MBO) is a recently developed evolutionary algorithm which has been used in many optimization problems. Migration and adjusting operators of MBO have a significant effect on the performance of it. These two operators change candidate variables of each individual ...
The feature vector is optimized by an Improved Monarch Butterfly optimization (IMBO) algorithm to reduce the dimensionality. These optimized features are applied to convolution neural networks to classify signals. The experimental results of the proposed method give a 99.49% accuracy, 99.58% sensitivity...
Section 5 gives the information about the Epsilon-fuzzy dominance-based Multi-Objective Improved Monarch Butterfly Optimization algorithm. Section 6 mentioned the usage of the simulation environment and the performance evaluation of the proposed approach. Finally, the last section contributes to the ...