Optimization is essentially everywhere and most real-world problems are of nonlinear and multimodal nature, i.e., there may exist multiple local optima that become traps for local search [23]. That is, classical local search based on gradient descent will get stuck in local optima unless restart mechanisms or search space exploration methods prevent premature convergence. Much effort has been put into this issue. Early attempts tried to make local search more flexible, e.g., by adding search points or spanning simplex structures, to discover patterns in search space and allow non-derivative descent to the optimum [20]. However, local search cannot solve these problems in general. Thus, later approaches [1] combine originally one-dimensional global search mechanisms like the STEP global search [30] and a local interpolation technique proposed by Brent [3] for the multivariate case. Others combine established stochastic global search mechanisms based on clustering [24] with newer elements of global optimizers [29] to gain quality improvements of solutions and to avoid finding only local optima [22].

Combinatorial problems which have been proven to be NP-hard are faced in Higher Education Institutions and researches have extensively investigated some of the well-known combinatorial problems such as the timetabling and student project allocation problems. However, NP-hard problems faced in Higher Education Institutions are not only confined to these categories of combinatorial problems. The majority of NP-hard problems faced in institutions involve grouping students and/or resources, albeit with each problem having its own unique set of constraints. Thus, it can be argued that techniques to solve NP-hard problems in Higher Education Institutions can be transferred across the different problem categories. As no method is guaranteed to outperform all others in all problems, it is necessary to investigate heuristic techniques for solving lesser-known problems in order to guide stakeholders or software developers to the most appropriate algorithm for each unique class of NP-hard problems faced in Higher Education Institutions. To this end, this study described an optimization problem faced in a real university that involved grouping students for the presentation of semester results. Ordering based heuristics, genetic algorithm and the ant colony optimization algorithm implemented in Python programming language were used to find feasible solutions to this problem, with the ant colony optimization algorithm performing better or equal in 75% of the test instances and the genetic algorithm producing better or equal results in 38% of the test instances.

The difficulty of classical planning increases exponentially with search-tree depth. Heuristic search can make planning more efficient, but good heuristics can be expensive to compute or may require domain-specific information, and such information may not even be available in the more general case of black-box planning. Rather than treating a given planning problem as fixed and carefully constructing a heuristic to match it, we instead rely on the simple and general-purpose "goal-count" heuristic and construct macro-actions to make it more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Our method discovers macros that dramatically improve black-box planning efficiency across a wide range of planning domains, including Rubik's cube, where it generates fewer states than the state-of-the-art LAMA planner with access to the full SAS$^+$ representation.

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%.

Neural Module Network (NMN) is a machine learning model for solving the visual question answering tasks. NMN uses programs to encode modules' structures, and its modularized architecture enables it to solve logical problems more reasonably. However, because of the non-differentiable procedure of module selection, NMN is hard to be trained end-to-end. To overcome this problem, existing work either included ground-truth program into training data or applied reinforcement learning to explore the program. However, both of these methods still have weaknesses. In consideration of this, we proposed a new learning framework for NMN. Graph-based Heuristic Search is the algorithm we proposed to discover the optimal program through a heuristic search on the data structure named Program Graph. Our experiments on FigureQA and CLEVR dataset show that our methods can realize the training of NMN without ground-truth programs and achieve superior efficiency over existing reinforcement learning methods in program exploration.

The main goal of the multitasking optimization paradigm is to solve multiple and concurrent optimization tasks in a simultaneous way through a single search process. For attaining promising results, potential complementarities and synergies between tasks are properly exploited, helping each other by virtue of the exchange of genetic material. This paper is focused on Evolutionary Multitasking, which is a perspective for dealing with multitasking optimization scenarios by embracing concepts from Evolutionary Computation. This work contributes to this field by presenting a new multitasking approach named as Coevolutionary Variable Neighborhood Search Algorithm, which finds its inspiration on both the Variable Neighborhood Search metaheuristic and coevolutionary strategies. The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted. This paper pioneers on the simultaneous solving of this kind of tasks. Two different multitasking scenarios are considered, each comprising 11 graph instances. Results obtained by our method are compared to those issued by a parallel Variable Neighborhood Search and independent executions of the basic Variable Neighborhood Search. The discussion on such results support our hypothesis that the proposed method is a promising scheme for simultaneous solving community detection problems over graphs.

This study focuses on the design and development of methods for generating cargo distribution plans for large-scale logistics networks. It uses data from three large logistics operators while focusing on cross border logistics operations using one large graph. The approach uses a three-step methodology to first represent the logistic infrastructure as a graph, then partition the graph into smaller size regions, and finally generate cargo distribution plans for each individual region. The initial graph representation has been extracted from regional graphs by spectral clustering and is then further used for computing the distribution plan. The approach introduces methods for each of the modelling steps. The proposed approach on using regionalization of large logistics infrastructure for generating partial plans, enables scaling to thousands of drop-off locations. Results also show that the proposed approach scales better than the state-of-the-art, while preserving the quality of the solution. Our methodology is suited to address the main challenge in transforming rigid large logistics infrastructure into dynamic, just-in-time, and point-to-point delivery-oriented logistics operations.

This paper introduces and studies a graph-based variant of the path planning problem arising in hostile environments. We consider a setting where an agent (e.g. a robot) must reach a given destination while avoiding being intercepted by probabilistic entities which exist in the graph with a given probability and move according to a probabilistic motion pattern known a priori. Given a goal vertex and a deadline to reach it, the agent must compute the path to the goal that maximizes its chances of survival. We study the computational complexity of the problem, and present two algorithms for computing high quality solutions in the general case: an exact algorithm based on Mixed-Integer Nonlinear Programming, working well in instances of moderate size, and a pseudo-polynomial time heuristic algorithm allowing to solve large scale problems in reasonable time. We also consider the two limit cases where the agent can survive with probability 0 or 1, and provide specialized algorithms to detect these kinds of situations more efficiently.

The combinatorial auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing. It can obtain high economic efficiency and user flexibility by allowing bidders to submit bids for combinations of different items instead of only for individual items. However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i.e., the winner determination problem (WDP), is NP-complete to solve and inapproximable. Existing works for WDPs are generally based on mathematical optimization techniques and most of them focus on the single-unit WDP, where each item only has one unit. On the contrary, few works consider the multi-unit WDP in which each item may have multiple units. Given that the multi-unit WDP is more complicated but prevalent in cloud computing, we propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss. Specifically, we model the multi-unit WDP as an augmented bipartite bid-item graph and use a graph neural network (GNN) with half-convolution operations to learn the probability of each bid belonging to the optimal allocation. To improve the sample generation efficiency and decrease the number of needed labeled instances, we propose two different sample generation processes. We also develop two novel graph-based post-processing algorithms to transform the outputs of the GNN into feasible solutions. Through simulations on both synthetic instances and a specific virtual machine (VM) allocation problem in a cloud computing platform, we validate that our proposed method can approach optimal performance with low complexity and has good generalization ability in terms of problem size and user-type distribution.

AutoML is a generic expression to indicate pieces of software that perform Machine Learning tasks automatically. Such pieces of software can be Python libraries like Auto-Sklearn or software programs like Data Robot. AutoML pieces of software replace all the boring steps that take more time to a Data Scientist's work. They actually make all the combinations of the several parameters of a pipeline (e.g. the blank filling values, scaling algorithm, model type, model hyperparameters) and select the best combination that maximizes some performance metrics (like RMSE or Area under the ROC Curve) in k-fold cross-validation using some search algorithm (like Grid or Random Search). They can really simplify the life of somebody that has to create a model from scratch and sometimes they explore combinations and scenarios that a Data Scientist may not have thought of.