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This document deals with a variant of the famous NP-hard () vehicle routing problem called the Vehicle Routing Problem with Simultaneous Pickup and Delivery VRPSPD. In VRPSPD, the customer makes simultaneous demand of pickup and delivery. The delivery is made from a single depot, while the collection is done throughout the trip. Vehicles providing the service have limited capacities. In each route, the charge of a vehicle must not exceed its capacity at any time. The authors will present the work that has led to the development of a technique for solving VRPSPD. This technique is based on a cooperative approach reaping the benefits of three metaheuristics to reach a solution to the problem of advantageous quality.

Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd'99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

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