Adaptive two-SVM multi-objective cuckoo search algorithm for software defect prediction

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2015.04.005 ◽

2015 ◽

Vol 18 (4) ◽

pp. 603-615 ◽

Cited By ~ 32

Author(s):

M. Balasubbareddy ◽

S. Sivanagaraju ◽

Chintalapudi V. Suresh

Keyword(s):

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Multi Objective Optimization ◽

Multi Objective ◽

Practical Constraints

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Prediction of Software Defect Using Ensemble Learning Based Improved Sparrow Search Algorithm To Optimize Extreme Learning Machine

10.21203/rs.3.rs-1100298/v1 ◽

2021 ◽

Author(s):

Yu Tang ◽

Qi Dai ◽

Mengyuan Yang ◽

Lifang Chen

Keyword(s):

Extreme Learning Machine ◽

Ensemble Learning ◽

Learning Algorithm ◽

Search Algorithm ◽

Defect Prediction ◽

Software Defect Prediction ◽

Prediction Ability ◽

Software Defect ◽

Ensemble Learning Algorithm ◽

Learning Machine

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

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