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Taking inspiration from natural behaviors to devise new optimization algorithms has played an important role in the history of the field of metaheuristics (Sörensen et al. 2017). Unfortunately, in the last two decades we have been witnessing a new trend by which dozens of metaphor-based metaheuristics based on the most diverse possible set of natural, artificial, social, and sometimes even supernatural phenomena and behaviors are proposed, without a clear motivation beyond the desire of their authors to publish their papers.

The problem of maximizing the lifetime of a wireless sensor network which uses video cameras to monitor targets is considered. These video cameras can rotate and have a fixed monitoring angle. For a target to be covered by a video camera mounted on a sensor node, three conditions must be satisfied. First, the distance between the sensor and the target should be less than the sensing range. Second, the direction of the camera sensor should face the target, and third, the focus of the video camera should be such that the picture of the target is sharp. Basic elements on optics are recalled, then some properties are shown to efficiently address the problem of setting the direction and focal distance of a video camera for target coverage. Then, a column generation algorithm based on these properties is proposed for solving three lifetime maximization problems. Targets are considered as points in the first problem, they are considered as discs in the second problem (which allows for considering occlusion) and in the last problem, focal distance is also dealt with for taking image sharpness into account. All of these problems are compared on a testbed of 180 instances and numerical results show the effectiveness of the proposed approach.

In this paper, an unrelated parallel machine scheduling problem with job (product) and machine acceptance and renewable resource constraints was considered. The main idea of this research was to establish a production facility without (or with minimum) investment in machinery, equipment, and location. This problem can be applied to many real problems. The objective was to maximize the net profit; that is, the total revenue minus the total cost, including fixed costs of jobs, job transportation costs, renting costs of machines, renting cost of resources, and transportation costs of resources. A mixed-integer linear programming (MILP) model and several heuristics (greedy, GRASP, and simulated annealing) are presented to solve the problem.

The minimum sitting arrangement (MinSA) problem is a linear layout problem consisting in minimizing the number of errors produced when a signed graph is embedded into a line. This problem has been previously tackled by theoretical and heuristic approaches in the literature. In this paper we present a basic variable neighborhood search (BVNS) algorithm for solving the problem. First, we introduce a novel constructive scheme based on the identification of cliques from the input graph, when only the positive edges are considered. The solutions obtained by the constructive procedure are then used as a starting point for the proposed BVNS algorithm. Efficient implementations of the several configurations of the local search procedure within the BVNS are described. The algorithmic proposal is then compared with previous approaches in the state of the art for the MinSA over different sets of referred instances. The obtained results supported by non-parametric statistical tests, indicate that BVNS can be considered as the new state-of-the-art algorithm for the MinSA.

Embedded systems are everywhere in contemporary life and are supposed to make our lives more comfortable. In industry, embedded systems are used to manage and control complex systems (e.g. nuclear power plants, telecommunications and flight control) and they are also taking an important place in our daily activities (e.g. smartphones, security alarms and traffic lights). In the design of embedded systems, memory allocation and data assignment are among the main challenges that electronic designers have to face. In fact, they impact heavily on the main cost metrics (power consumption, performance and area) in electronic devices. Thus designers of embedded systems have to pay careful attention in order to minimize memory requirements, thus improving memory throughput and limiting the power consumption by the system's memory. Electronic designers attempt to minimize memory requirements with the aim of lowering the overall system costs. A state of the art of optimization techniques for memory management and data assignment is presented in this book.

We provide a catalogue of 17 700 unique coastal-based instances distributed throughout the globe and derived from bathymetric and topographic data made publicly available by the General Bathymetric Chart of the Oceans (GEBCO) as of 2022. These instances, or digital elevation models (DEMs), are delivered in the form of raster grids with a 15 arcsec resolution and are divided equally into three libraries, namely A, B, and C. In a given library, the dimensions range from a minimum of 10×10 cells to a maximum of 300×300 cells, with an incremental step of 5, i.e. 59 unique dimensions with 100 instances per dimension. In addition, for each dimension, these instances are ordered by increasing number of maritime cells and have in common the presence of a unique maritime-connected component with a ratio of maritime cells lying between 25 % and 95 % so as to cover a broad spectrum of different coastline geometries. In this paper, we will describe in detail the procedure used for their automated generation. The resulting catalogue can be downloaded from Zenodo, a general-purpose repository operated by CERN (European Organisation for Nuclear Research) and developed under the European OpenAIRE programme, at the following persistent address: https://doi.org/10.5281/zenodo.10530247 (Thuillier et al., 2024c). Additionally, a set of 18 colour palettes specifically designed for the visualisation of DEMs has been derived for this occasion and is available at the following address: https://doi.org/10.5281/zenodo.10530296 (Thuillier et al., 2024e). Both of these repositories come with comprehensive documentation.

Memory allocation has a significant impact on power consumption in embedded systems. We address the dynamic memory allocation problem, in which memory requirements may change at each time interval. This problem has previously been addressed using integer linear programming and iterative approaches which build a solution interval by interval taking into account the requirements of partial time intervals. A GRASP that builds a solution for all time intervals has been proposed as a global approach. Due to the complexity of this problem, the GRASP algorithm solution quality decreases for larger instances. In order to overcome this drawback, we propose a multiple neighborhood search hybridized with a Tabu Search and enhanced by complex ejection chains. The proposed approach outperforms all previously developed methods devised for the dynamic memory allocation problem.

In the design of electronic embedded systems, the allocation of data structures to memory banks is a main challenge faced by designers. Indeed, if this optimization problem is solved correctly, a great improvement in terms of efficiency can be obtained. In this paper, we consider the dynamic memory allocation problem, where data structures have to be assigned to memory banks in different time periods during the execution of the application. We propose a GRASP to obtain high quality solutions in short computational time, as required in this type of problem. Moreover, we also explore the adaptation of the ejection chain methodology, originally proposed in the context of tabu search, for improved outcomes. Our experiments with real and randomly generated instances show the superiority of the proposed methods compared to the state-of-the-art method.

Memory allocation in embedded systems is one of the main challenges that electronic designers have to face. This part, rather difficult to handle is often left to the compiler with which automatic rules are applied. Nevertheless, an optimal allocation of data to memory banks may lead to great savings in terms of running time and energy consumption. This paper introduces an exact approach and a vns -based metaheuristic for addressing a memory allocation problem. Numerical experiments have been conducted on real instances from the electronic community and on dimacs instances expanded for our specific problem.

We propose a feature-based guidance mechanism to enhance metaheuristic algorithms for solving the Capacitated Vehicle Routing Problem (CVRP). This mechanism leverages an Explainable AI (XAI) model to identify features that correlate with high-quality solutions. These insights are used to guide the search process by promoting solution diversity and avoiding premature convergence. The guidance mechanism is first integrated into a custom metaheuristic algorithm, which combines neighborhood search with a novel hybrid of the split algorithm and path relinking. Experiments on benchmark instances with up to \\(30,000\\) customer nodes demonstrate that the guidance significantly improves the performance of this baseline algorithm. Furthermore, we validate the generalizability of the guidance approach by integrating it into a state-of-the-art metaheuristic, where it again yields statistically significant performance gains. These results confirm that the proposed mechanism is both scalable and transferable across algorithmic frameworks.