Software fault prediction (SFP) is a critical focus in software engineering, aiming to enhance productivity and minimize costs by detecting faults early. Feature selection (FS) is pivotal in SFP, enabling the identifi...
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Software fault prediction (SFP) is a critical focus in software engineering, aiming to enhance productivity and minimize costs by detecting faults early. Feature selection (FS) is pivotal in SFP, enabling the identification of pertinent features for fault prognosis. Existing Feature Selection methods face challenges such as high computational complexity and poor generalization. This paper introduces Feature Selection using spiderwaspoptimization (FSSWO), a novel FS approach employing the spiderwaspoptimization (SWO) algorithm, specifically designed for SFP. FSSWO selects optimal feature subsets inspired by spiderwasps' behavior. The proposed FSSWO approach is compared with several existing feature selection algorithms, namely FS using Genetic algorithm (FSGA), FS using Particle Swarm optimization (FSPSO), FS using Differential Evolution (FSDE), and FS using Ant Colony optimization (FSACO). Using eleven benchmark datasets, the performance of the proposed FSSWO technique has been assessed and contrasted with its equivalent. The results of the proposed FSSWO approach provide comparable and even superior results to the existing algorithms. The significance of the results has been statistically validated using Friedman and Holm tests. The statistical result of the proposed FSSWO approach reveals that the performance of proposed FSSWO models is improved which leads to better quality software at reduced costs.
Cognitive radio (CR) is an effective technology for addressing spectrum scarcity, which can improve the utilization of spectrum resources through intelligent sensing and dynamic parameter adjustment. Since traditional...
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Cognitive radio (CR) is an effective technology for addressing spectrum scarcity, which can improve the utilization of spectrum resources through intelligent sensing and dynamic parameter adjustment. Since traditional resource allocation algorithms are difficult to adapt to the dynamic characteristics of cognitive radio environment, more and more researchers are focusing on intelligent optimizationalgorithms. Our objective is to maximize the channel capacity of cognitive transmitters under interference constraint at primary receiver, total transmit power constraint and fairness constraint in underlay cognitive radio networks. To enhance the flexibility of the algorithm, we transform the original constrained optimization problem into an unconstrained penalty function form. Given that the proposed problem is non-convex, we present the spiderwaspoptimization (SWO) algorithm to solve this optimization problem. To better search the solution space and avoid getting trapped in local optima, a hybrid spider wasp optimization algorithm (HSWO) is proposed. This algorithm integrates genetic algorithm (GA) principles to help the SWO algorithm in achieving the global optimum. Additionally, three different dynamic response strategies were proposed to validate the adaptability and flexibility of the proposed algorithm in dynamic environments. Simulation results show that HSWO and SWO algorithm can obtain higher system capacity and higher flexibility compared with the particle swarm optimization (PSO).
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