Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi-objective cuckoo search under-sampled software defect prediction model based on SVM (HMOCS-US-SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi-objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non-defective sampling and optimize the parameters of SVM. Then, three under-sampled methods for decision region range are proposed to select the non-defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G-mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model.Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem. KEYWORDSclass imbalance, hybrid multi-objective cuckoo search, software defect prediction, SVM, under-sampled INTRODUCTIONWith the advancement of network society, the software has been applied widely in the areas of life, such as the banking systems, biopharmaceutical engineering, and traffic signal command. Therefore, an increasing number of attention has been paid to the quality of software products. 1Generally speaking, software quality mainly includes five aspects: reliability, understandability, availability, maintainability, and effectiveness. 2 It is specially said that the reliability plays an important factor in leading to the software defects. 3Software defects are the errors in the software development, which will lead to faults, failure, collapse, and even endanger the safety of human life and property. 4 Therefore, how to find defects as much as possible is particularly important. The core of software defect prediction (SDP) 5 is to extract the characteristic attributes as the obvious defect tendency of the historical software module, so as to predict the type or number of defects in the new software projects.Class imbalance (CIB) in datasets is an unavoidable problem in SDP, which shows that 80% of the defects are concentrated on 20% of the modules. 6 However, the traditional classification algorithm 7 is built on the relative balance of datasets, which not suitable for imbalanced datasets. It does mean that the classification algorithm is more inclined to the non-defected module. 8 Therefore, how to alleviate the imbalance of datasets is a major problem in SDP. To tackle the CIB problem, the existing research can be roughly divided into cost-sensitive method, 9 ensemble method, 10 and sampling method. 11• Cost-sensitive algorithms 12 solve the imbalanced problems by modifying algorithms, which means that the method improves the accuracy of classificatio...

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Summary Wireless sensor networks (WSN) have high value in the field of wireless communications. As the earliest WSN clustering protocol, Low Energy Adaptive Clustering Hierarchy (LEACH) can effectively reduce the energy consumption of data transmission in sensor networks. However, LEACH has some problems such as cluster head nodes are unevenly distributed. In this paper, a unified heuristic bat algorithm (UHBA) is proposed to optimize elections in cluster heads. This algorithm guarantees that the election of cluster heads can freely transform both global search and local search. Meanwhile, comparing with several other variants of the bat algorithm in CEC2013 test suite, it can be seen from results that UHBA has better performance. Moreover, the application of the algorithm on LEACH is better than other algorithms, which further proves that the algorithm has better results.

Task scheduling problem refers to how to reasonably arrange many tasks provided by users in virtual machines, which is very important in the cloud computing. And the quality of the scheduling performance directly affects the customer satisfaction and the provider benefits. In order to describe the task scheduling problem of cloud computing more precisely and improve the scheduling performance. This paper establishes many-objective cloud model, including four objectives: minimizing time, minimizing costs, maximizing resource utilization, and balancing load. At the same time, a many-objective optimization algorithm based on hybrid angles (MaOEA-HA) is proposed to solve this model. Hybrid angle strategy is designed to optimize the algorithm better, which combines two angle strategies: individual-to-individual angle and individual-to-reference point angle. One by one elimination strategy was introduced to remain individuals with better performance. By comparing with five other advanced many-objective optimization algorithms, MaOEA-HA shows the best performance on the DTLZ test suite. Moreover, different algorithms are applied to solve the cloud task scheduling problem, and MaOEA-HA algorithm achieves best results.INDEX TERMS Cloud computing, hybrid-angle strategy, many-objective algorithm, task scheduling.