Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker’s preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.

In network-centric warfare, the interconnections among various combat resources enable an advanced operational pattern of cooperative engagement. The operational effectiveness and outcome strongly depends on the reasonable utilization of available sensors and weapons. In this paper, a mathematical model for the coallocation of sensors and weapons is built, taking into account the interdependencies between weapons and sensors, the resource constraints, the capability constraints, as well as the strategy constraints. A marginal-return-based constructive heuristic (MRBCH) is proposed to solve the formulated sensor-weapon-target assignment (S-WTA) problem. MRBCH exploits the marginal return of each sensor-weapon-target triplet and dynamically updates the threat value of all targets. It relies only on simple lookup operations to choose each assignment triplet, thus resulting in very low computational complexity. For performance evaluation, we build a general Monte Carlo simulation-based S-WTA framework. Furthermore, we employ a random sampling method and an extension of the state-of-the-art algorithm Swt_opt as competitors. The computational results show that MRBCH consistently performs very well in solving S-WTA instances of different scales, and it can generate assignment schemes much more efficiently than its competitors.

To address the large-scale and time-consuming multiple agile earth observation satellite (multi-AEOS) scheduling problems, this paper proposes a data-driven parallel scheduling approach, which is composed of a probability prediction model, a task assignment strategy and a parallel scheduling manner. In this approach, given the historical data of satellite scheduling, a prediction model is trained based on the Cooperative Neuro-Evolution of Augmenting Topologies (C-NEAT) to predict the probabilities that a task will be fulfilled by different satellites. Driven by the probability prediction model, an assignment strategy is adopted for dividing the multi-AEOS scheduling problem into several single-AEOS scheduling subproblems, which can adaptively assign each task to the satellite with the highest predicted probability and greatly decrease the problem size. In a parallel manner, the single-AEOS scheduling subproblems are optimized respectively, leading to an acceleration in the optimization efficiency of the original problem. Computational experiments indicate that the proposed approach presents better overall performance than other state-of-the-art methods within a very limited scheduling time. As the two main components of the proposed approach, the prediction model based on C-NEAT and the task assignment strategy also outperform other models with traditional training algorithms and inadaptive assignment strategies, respectively.

As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil

This article investigates the tracking-oriented robust leaderless time-varying formation (TVF) control problem for unmanned aerial vehicle swarm systems (UAVSSs) with Lipschitz nonlinear dynamics under directed topology, where external disturbances are random and bounded, and communication delays (CDs) are bounded. In this article, a state-feedback control approach is adopted to make sure that a UAVSS forms a desired TVF and follows a specified trajectory when CDs and external disturbances occur. First, a novel PD-like formation control protocol with several unknown parameters and CDs is designed. The protocol contains the information of the local neighborhood status and its differential quantities. Second, the tracking-oriented robust leaderless TVF control problem with Lipschitz dynamics, external disturbances, and CDs is transformed into a problem about asymptotic stability of a lower dimensional closed-loop control system through a special matrix decomposition. Third, a theorem is proposed to determine the unknown parameters of the control protocol and the upper bound of CDs. In the theorem, sufficient conditions for a UAVSS to attain the anticipated TVF and trajectory tracking are obtained. A Lyapunov–Krasovskii (LK) functional is constructed to verify that the error among the practical flight state of UAVs, the anticipant TVF configuration, and tracking trajectory can asymptotically converge to 0. Finally, with the presentation of a simulation case, the effectiveness of the theoretical results is illustrated.