In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing... Show More

In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing Bees Algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with... Show More

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

African Buffalo Optimization (ABO) is one of the most recent bioinspired metaheuristics based on swarm intelligence. It is inspired by the buffalo's behavior and lifestyle. ABO Metaheuristic showed... Show More

African Buffalo Optimization (ABO) is one of the most recent bioinspired metaheuristics based on swarm intelligence. It is inspired by the buffalo's behavior and lifestyle. ABO Metaheuristic showed its effectiveness for solving several optimization problems. In this contribution, we present an adaptive ABO for solving the NP-hard one dimensional Bin Packing Problem (1BPP). In the proposed algorithm, we used the ABO algorithm in combination with Ranked Order Value method to obtain discrete values and Bin Packing Problem heuristics to incorporate the problem knowledge. The proposed algorithm is used to solve 1210 of 1BPP instances. The obtained results are compared with those found by recent algorithms in the literature. Computational results show the effectiveness of the proposed algorithm and its ability to achieve best and promising solutions.

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud... Show More

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.