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Liner shipping is vulnerable to many disruptive factors such as port congestion or harsh weather, which could result to delay in arriving at the ports. It could result in both financial and reputation losses. The vessel schedule recovery problem (VSRP) is concerned with different possible actions to reduce the effect of disruption. In this work, we are concerned with speeding up strategy in VSRP, which is called the speed-based vessel schedule recovery problem (S-VSRP). We model S-VSRP as a multiobjective optimization (MOO) problem and resort to several multiobjective evolutionary algorithms (MOEAs) to approximate the optimal Pareto set, which provides vessel route-based speed profiles. It gives the stakeholders the ability to tradeoff between two conflictive objectives: total delay and financial loss. We evaluate the problem in three scenarios (i.e., scalability analysis, vessel steaming policies, and voyage distance analysis) and statistically validate their performance significance. According to experiments, the problem complexity varies in different scenarios, and NSGAII performs better than other MOEAs in all scenarios.

Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the rough information granules are built. In this paper, we cast the RCN model within the framework of fuzzy rough sets in an attempt to eliminate the need for a user-specified similarity threshold while retaining the model's discriminatory power. As far as we know, this is the first study that brings fuzzy sets into the domain of rough cognitive mapping. Numerical results in the presence of 140 well-known pattern classification problems reveal that our approach, referred to as Fuzzy-Rough Cognitive Networks, is capable of outperforming most traditional classifiers used for benchmarking purposes. Furthermore, we explore the impact of using different heterogeneous distance functions and fuzzy operators over the performance of our granular neural network.

The aggregation of preferences (expressed in the form of rankings) from multiple experts is a well-studied topic in a number of fields. The Kemeny ranking problem aims at computing an aggregated ranking having minimal distance to the global consensus. However, it assumes that these rankings will be complete, i.e., all elements are explicitly ranked by the expert. This assumption may not simply hold when, for instance, an expert ranks only the top-K items of interest, thus creating a partial ranking. In this paper we formalize the weighted Kemeny ranking problem for partial rankings, an extension of the Kemeny ranking problem that is able to aggregate partial rankings from multiple experts when only a limited number of relevant elements are explicitly ranked (top-K), and this number may vary from one expert to another (top-Ki). Moreover, we introduce two strategies to quantify the weight of each partial ranking. We cast this problem within the realm of combinatorial optimization and lean on the successful Ant Colony Optimization (ACO) metaheuristic algorithm to arrive at high-quality solutions. The proposed approach is evaluated through a real-world scenario and 190 synthetic datasets from www.PrefLib.org. The experimental evidence indicates that the proposed ACO-based solution is capable of significantly outperforming several evolutionary approaches that proved to be very effective when dealing with the Kemeny ranking problem. The authors would like to thank Ph.D. Student Isel Grau from Vrije Universiteit Brussel, Belgium, for her valuable support on designing the membership measure and running the algorithms. This work was supported by the Research Council of Hasselt University.

Rough Cognitive Networks are granular classifiers stemming from the hybridization of Fuzzy Cognitive Maps and Rough Set Theory. Such cognitive neural networks attempt to quantify the impact of rough granular constructs (i.e., the positive, negative and boundary regions of a target concept) over each decision class for the problem at hand. In rough classifiers, determining the precise granularity level is crucial to compute high prediction rates. Regrettably, learning the similarity threshold parameter requires reconstructing the information granules, which may be time-consuming. In this paper, we put forth a new multiclassifier system classifier named Rough Cognitive Ensembles. The proposed ensemble employs a collection of Rough Cognitive Networks as base classifiers, each operating at a different granularity level. This allows suppressing the requirement of learning a similarity threshold. We evaluate the granular ensemble with 140 traditional classification datasets using different heterogeneous distance functions. After comparing the proposed model to 15 well-known classifiers, the experimental evidence confirms that our scheme yields very promising classification rates. This work was supported by the Research Council of Hasselt University. The authors would like to thank the anonymous reviewers for their constructive remarks throughout the revision process.

Graphical abstractDisplay Omitted HighlightsWe adapted two versions of the recently proposed Cuckoo Search metaheuristic for use in the combinatorial optimization realm.We have applied the two adapted Cuckoo Search implementations to system-level fault diagnosis.We have experimentally validated the optimization capabilities of these schemes and compared them to particle swarm optimization, dynamic mesh optimization, and firefly algorithm.Tests were performed across three different distributed diagnosis models under systems of small, medium, and large sizes.After a rigorous statistical analysis, we conclude that cuckoo search algorithms offer superior performance, especially YD-CS. System-level fault diagnosis (SLFD) deals with the detection of all faulty nodes in a set of networked units in a parallel or distributed system. By allowing nodes to test each other under well-defined conditions and carefully inspecting the collection of test outcomes, the true set of damaged nodes can be located. In this paper, we tackle the SLFD problem by using a recently proposed bio-inspired optimization method: the Cuckoo Search. Two formalizations of this scheme were independently put forth by Yang and Deb 16] and Rajabioun 17] for numerical optimization. In this work, we have adapted both methods to work in the combinatorial search space induced by SLFD and we have tested them against other nature-inspired metaheuristics in presence of a class of distributed systems called t-diagnosable systems, since they are easy to generate and eliminate ambiguity in the optimization-driven fault diagnosis. Three well-known test models (i.e. PMC, symmetric and asymmetric comparison) that define how the system units test one another were employed in the simulations. The empirical results reveal that the two cuckoo-based approaches outperformed their competitors in terms of solution quality and spatio-temporal requirements, with Yang and Deb's version achieving notable improvements over Rajabioun'.