Metaheuristics are optimization techniques that can be adapted to different problem domains. They are considered general algorithms that have been successful in solving various problems. However, the increase in redundant ‘novel’ metaheuristics lacking solid validation has become a criticized trend in the research area. In this work, we present a software tool offering interactive development and metaheuristics customization. This software is based on a standard and theoretical framework for analyzing, modifying, and generating metaheuristics. We take advantage of this framework to examine some algorithms recently reported as innovative in the literature and reveal their true nature. Plus, these tools enable rapid prototyping of metaheuristics and allow for static validation experimentation and comparison of algorithms. It helps in the refinement and fine-tuning of the metaheuristics. We present a case study demonstrating the proposed tool's effectiveness, which promises influential scientific contributions and unlocks the potential of metaheuristics.

In this study, we assess the performance of var-ious machine learning algorithms in weather forecasting for Monterrey, Mexico, a large metropolitan area with a mountain-influenced semi-arid climate whose geographical features significantly hinder weather prediction. To tackle this problem, we propose using heterogeneous models: Random Forest Regression, Support Vector Regression, Gradient Boosting Regression, and Long Short-Term Memory neural networks. Our research targets seven crucial weather parameters and utilizes lagged feature sets and an iterative prediction model to implement these ML models for forecasting weather conditions several days in advance. We compare the performance of our models via several metrics and statistical tests. Our findings reveal unexpected patterns, such as improved model accuracy over extended forecast periods. However, we find promising results for establishing the basis for future work on weather forecasting in mountain-influenced semi-arid climates.

Heat sinks are a prevalent and direct solution for addressing the Microelectronic Thermal Management Problem (MTMP), which is critical in today's electronic industry. Specifi-cally, an optimally designed thermodynamic heat sink ensures that microelectronics operate reliably without compromising their lifespan and performance, thereby indirectly safeguarding user safety. Although Metaheuristics (MHs) have proven effective in tackling this complex design challenge due to their robust characteristics, no single MH consistently delivers superior per-formance across all scenarios. The study explores the feasibility of an Automated Metaheuristic Design strategy, employing a hyper-heuristic search to develop a population-based, metaphor-free MH specifically for the MTMP. Various scenarios are assessed by varying the heat sink design specifications and benchmarking the custom MH designs against several state-of-the-art MHs. The findings of this preliminary work provide statistical evidence that the tailored MHs surpass the performance of established MHs in these scenarios. A toolkit of MH components is assembled, which can be customized to construct MHs specifically for MTMPs. This approach enables practitioners to select the most suitable solver for a particular problem without needing extensive expertise in heuristic-based optimization.

Effective optimization of model variables is essential yet demanding in engineering and industrial processes. Metaheuristics (MHs) offer a proficient approach, but their design and tuning incorporate notable challenges. Automated Algorithm Design (AAD) methodologies provide a solution by enabling automated algorithm construction. This study utilizes a Hyper-Heuristic (HH) framework within AAD, which obtains a tailored MH to optimize a Proportional, Integral, and Derivative (PID) controller in an Automatic Voltage Regulation (AVR) system. We identify a preference for search operators from Spiral Dynamic, Swarm Dynamic, and Differential Mutation families, offering valuable insights for MH algorithm design in complex electrical systems. Our contributions include a novel methodology for distilling search operators for specific problem families and presenting effective search operators for MHs in electrical engineering scenarios. The study highlights the importance of precise controller tuning, demonstrated through the effectiveness of the tailored MH compared to others.

Microgrids stand as an alternative for incorporating Renewable Energy Sources into the electrical grid, but they require an adequate control scheme. Although the literature contains plenty of alternatives, it lacks implementations of hybrid controllers based on Hyper-Heuristics (HHs). Hence, we analyze whether they are of benefit. Our goal is simple: to alternate through diverse controllers as the simulation progresses. To this end, we consider some simple sequence-based selection HHs and test them across 13 scenarios. Instead of the customary low-level heuristics, we use predefined controllers that were previously tuned through a Genetic Algorithm. For the most part, at least one of the proposed models outperforms the best available controller. Thus, using HHs as an advanced control scheme seems feasible and should be explored more deeply in future works.

Hyper-heuristics are a broad topic that has drawn increasing attention because of its flexibility. This, however, implies that there are diverse models, including selection hyper-heuristics, where the idea is to derive a model that learns when to use each available solver. Nonetheless, such a learning procedure usually proves difficult and leads to non-ideal selections. Hence, in this work, we propose a recursive hyper-heuristic model allowing more complexity within the selection models. Our idea is straightforward: to have a selection hyper-heuristic to select low-level heuristics and lower-level hyper-heuristics. In doing so, one can merge the combined decisions of existing solvers. We test the feasibility of such a model through experiments on the Job Shop Scheduling Problem that cover small and large datasets of previously tailored instances. We found that increasing the order of the model leads to more stable and better-performing approaches. For example, migrating from a second-order hyper-heuristic to a fourth-order hyper-heuristic reduced the makespan by over 6%. Thus, the proposed model seems feasible and should be further tested under more varied scenarios and conditions.

Metaheuristics are commonly used to solve complex and challenging problems, particularly in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to select from, which can be overwhelming for a practitioner with solid experience in controlling electrical systems. Still, it is well-known that empirical or analytical tuning of controllers is no trivial labor. This work implements a methodology to address the Metaheuristic Composition Optimization Problem to coin a heuristic-based technique that guides an initially random population of individuals to find the optimal set of parameters of an Adaptive Sliding Mode Controller in the search space. As a case study, we implement this methodology for controlling the dynamic response of a DC-DC Buck-Boost converter under different conditions, including nonlinear perturbations. The objective is clear: find the optimal heuristic-based solver to determine the control parameters satisfying a predefined performance. Our experimental results reveal the reliability and potential of the proposed methodology when finding suitable solutions for control system design applications. We also support our findings with statistical analyses performed on the results obtained by the tailored metaheuristic against other classical heuristic-based methods.

It is no secret that optimisation is a popular topic in any practical engineering application. Similarly, Metaheuristics (MHs) are a fairly standard approach for solving optimisation problems due to their success, flexibility, and simplicity. However, it is seldom easy to find a solver from the overpopulation of metaheuristics that adequately deals with a given problem. For that reason, the solver selection is even considered an additional problem in many optimisation scenarios. This work investigates the Metaheuristic Composition Optimisation Problem, which involves designing heuristic-based procedures that solve continuous optimisation problems. Therefore, we propose two novel and still simple methodologies based on transfer learning to facilitate the automatic generation of population-based and metaphor-less MHs by using search operators from the literature. To represent these solvers, we adopt our previously proposed unfolded MH model. The first strategy deals with the problem dynamically, building the sequence while solving the low-level problem. In contrast, the second one does it statically by generating the whole candidate sequence before implementing it. Results provide us with information to prove the feasibility of these approaches via experiments using 32 problems with four different characteristic groups and four dimensionalities and varying the number of agents (30, 50, and 100) employed by the search operators. We also remark that one can compare these two methodologies on performance, but we emphasise their potential usage depending on the general application environment.

Metaheuristics (MHs) are proven powerful algorithms for solving non-linear optimisation problems over discrete, continuous, or mixed domains. Applications have ranged from basic sciences to applied technologies. Nowadays, the literature contains plenty of MHs based on exceptional ideas, but often, they are just recombining elements from other techniques. An alternative approach is to follow a standard model that customises population-based MHs, utilising simple heuristics extracted from well-known MHs. Different approaches have explored the combination of such simple heuristics, generating excellent results compared to the generic MHs. Nevertheless, they present limitations due to the nature of the metaheuristic used to study the heuristic space. This work investigates a field of action for implementing a model that takes advantage of previously modified MHs by learning how to boost the performance of the tailoring process. Following this reasoning, we propose a hyper-heuristic model based on Artificial Neural Networks (ANNs) trained with processed sequences of heuristics to identify patterns that one can use to generate better MHs. We prove the feasibility of this model by comparing the results against generic MHs and other approaches that tailor unfolded MHs. Our results evidenced that the proposed model outperformed an average of 84 % of all scenarios; in particular, 89 % of basic and 77 % of unfolded approaches. Plus, we highlight the configurable capability of the proposed model, as it shows to be exceptionally versatile in regards to the computational budget, generating good results even with limited resources.

Metaheuristics are a fairly standard approach for solving optimisation problems due to their success, flexibility, and simplicity. However, there is a plethora of metaheuristics available, with different performance levels for various problems. This work proposes a methodology for designing heuristic-based procedures to solve continuous optimisation problems and study how the population size affects its performance. The technique comprises the well-known Simulated Annealing algorithm as a hyper-heuristic, and a heuristic sequence taken from unfolding the conventional scheme of population-based metaheuristics reported in the literature. Our results show that the proposed approach is a reliable alternative for tackling optimisation problems. We find exciting insights, according to our data, about this primary implementation when varying the population size in different challenging problems.

Literature is prolific with metaheuristics for solving continuous optimisation problems. But, in practice, it is difficult to choose one appropriately. Moreover, it is necessary to determine a good enough set of parameters for the selected approach. Hence, this work proposes a strategy based on a hyper-heuristic for tailoring population-based metaheuristics. Besides, our approach considers search operators from well-known techniques as building blocks for new ones. We test this strategy through four benchmark functions and by varying their dimensions. We obtain metaheuristics with diverse configurations. We observe a possible performance boost when two or more search operators are considered. This could be due to previously unexplored interactions between such operators.

This study presents an offline learning Simulated Annealing approach to generate a constructive hyper-heuristic evaluated through training and testing on a set of instances for solving the Job-Shop Scheduling problem. The generated hyperheuristic uses a range of state features to control a set of low-level constructive heuristics. A hyper-heuristic is represented in terms of a set of rules, where each rule contains a fixed set of values for the features in consideration and the low level heuristic to be invoked. At each constructive step, the `closest' rule is selected and then the corresponding constructive low level heuristic is applied. Our distance metric is the Euclidean distance between the values within the rule and the state features characterising the partial schedule along with the remaining jobs to be scheduled for the partial solution. In this paper, we study a set of features computed with various well-known metrics and different feature transformation methods for improving the characterization of the problem instances and solutions to Job-Shop Scheduling as a part of our approach. Eight different scenarios are evaluated on a set of randomly generated problem instances. Each scenario represents a distinct approach combining a different feature transformation applied during the training and testing phases. The empirical results show that transformations can improve the spread of feature values and the choice of the transformation methods is influential on the performance of the overall approach. A particular choice generates a slightly better performance when compared to the standard approach, which uses the original features at all times, indicating the potential of the proposed approach for the future studies.

Recent years have witnessed an escalating interest for methods that automatically adapt to different types of problems. In this regard, the term hyper-heuristics-heuristics that either select or generate new heuristics-is a relevant concept. Experimental evidence supports the idea that hyperheuristics can outperform single, isolated heuristics. However, commonly used hyper-heuristic models require several inputs. One of them is a set of features that accurately characterize the instances, which limits their applicability. Thus, in this work, we analyze how to implement a simple evolutionary algorithm to produce feature-independent hyper-heuristics. We compare its performance against that of simple heuristics, for the domain of the knapsack problem. Our research focuses on two elements: performance and frequency. In the former, we analyze how the performance of the learning stage varies across different scenarios. In the latter, we examine how frequently heuristics interact within the hyper-heuristic. We show that the proposed hyper-heuristic model solves most of the instances considered in this work. Moreover, it does so more efficiently than isolated heuristics. At the same time, the model offers a straightforward parameter setting and requires little or no problem characterization, which simplifies its use on new problem domains.