This paper addresses the computationally challenging edge-based facility location problem with the objective of minimizing total travel time while accommodating uniformly distributed demand on network edges. To enhance computational efficiency, the proposed method integrates differential evolution (DE) with three distinct surrogate models: random forest, extreme learning machines, and extreme gradient boosting. While the concept of distributed demand on network edges presents a more realistic depiction of location problems, the necessity of decomposing edges and assigning them to their nearest facilities increases the complexity of the problem at hand. Therefore, the development of an effective and efficient solution method is crucial, particularly in time-sensitive contexts where rapid decisions are essential. Empirical evaluations demonstrate the efficacy and efficiency of the proposed multi-surrogate approach when compared to traditional DE and a leading surrogate-based algorithm. The results illustrate superior computational performance while preserving solution quality across various benchmark functions.

This paper addresses the facility location problem in the context of multiple-server facilities subject to congestion. The objective is to select a subset of facilities from a pool of candidate locations in order to meet customers’ demands. Additionally, the number of servers allocated to each facility is treated as a decision variable, and the service time for each server follows an exponential distribution. As network location problems are known to be NP-hard, this study introduces a random forest as a surrogate model with differential evaluation to minimize the aggregate expected traveling times and aggregate expected waiting times of customers. The proposed algorithm is implemented and evaluated on a set of test problems with different sizes and specifications, demonstrating its high efficiency compared to differential evaluation.

As one of the most prominent variants of the facility location problem, the p-median problem aims to determine the best locations for establishing p number of facilities such that the aggregate customers' transportation cost is minimized. Since the p-median problem is classified as NP-hard, the application of metaheuristics to solve it is inevitable. Considering the fast development in metaheuristics, choosing the most appropriate algorithm to solve this problem is a difficult task. Therefore, this work presents a comparative study of several classical and recently developed nature-inspired optimization algorithms to solve the discrete uncapacitated p-median problem on several randomly generated test instances with different sizes and spec-ifications.

This study proposes a novel multi-objective integer programming model for a collision-free discrete drone path planning problem. Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and the accumulated maximum path risk simultaneously. The static environment is represented as 3D grid cells. Due to the NP-hardness nature of the problem, several state-of-the-art evolutionary multi-objective optimization (EMO) algorithms with customized crossover and mutation operators are applied to find a set of non-dominated solutions. The results show the effectiveness of applied algorithms in solving several generated test cases.

The amount of scientific conferences and journal articles continues to increase and new approaches are required to support users in finding relevant publications. This study investigates to what extent a new machine learning (ML) pipeline may preferentially identify links between similar scientific articles. The characteristics of intersections and unions of keywords, contextualized keywords (i.e., synsets) and neighbors are computed and used to train a ML model. Automated machine learning (AutoML) is then applied to ease the search for a new pipeline. Extensive experiments demonstrated that a newly designed ML model achieves an accuracy of 90% on a dataset of approximately 120,000 article pairs. These results suggest that application of ML for proposing new recommendation systems could have in the long term a positive impact in the literature.

The optimal camera placement problem is that of determining the best possible set of camera positions and orientations in order to meet application-specific constraints and objectives. This paper focuses on one application of the problem: global area surveillance. Given an area to be covered, the question is to design a camera network which fully covers critical subareas and proceeds in a best-effort manner in the rest of the environment, given a limited budget. This is achieved through the integration of user-provided input into a mixed combinatorial model which brings together two variants of a popular optimisation problem. Time-efficient algorithms then allow for regular user interaction in between solving iterations. This human-assisted design is based off requirements set by experts and decision makers and yields components of a decision support system to support law enforcement officers and officials when designing video surveillance infrastructure.

This paper considers multiobjective UAV path planning in a real 3D environment with the objective to find a safe energy-efficient path. An Enhanced Non-dominated Sorting Genetic Algorithm-II, called ENSGA-II, is proposed and combines several sorts of heuristic information to customize crossover and mutation operators. Furthermore, a local search and a ranking-based roulette wheel selection are incorporated for the mating procedure. Experiment results confirm that ENSGA-II has a better convergence rate and spread of solutions on several new real-world datasets. The effectiveness of the local search component is also validated on the CrazyS robot operating system (ROS) package which consists of a pelican quadcopter's modeling.

Neural architecture search (NAS) has achieved great success in different computer vision tasks such as object detection and image recognition. Moreover, deep learning models have millions or billions of parameters and applying NAS methods when considering a small amount of data is not trivial. Unlike computer vision tasks, labeling time series data for supervised learning is a laborious and expensive task that often requires expertise. Therefore, this paper proposes a simple-yet-effective fine-tuning method based on repeated k-fold cross-validation in order to train deep residual networks using only a small amount of time series data. The main idea is that each model fitted during cross-validation will transfer its weights to the subsequent folds over the rounds. We conducted extensive experiments on 85 instances from the UCR archive for Time Series Classification (TSC) to investigate the performance of the proposed approach. The experimental results reveal that our proposed model called NAS-T reaches new state-of-the-art TSC accuracy, by designing a single classifier that is able to beat HIVE-COTE: an ensemble of 37 individual classifiers.

Differential Evolution algorithm (DE) has been investigated in several studies. Indeed, it has been revealed that despite its successful search operators, DE may get trapped in local optimum due to the poor parameter configuration, and the inappropriate search operators. In this study, we introduce a resilient mutation strategy well-suited to real-world problems. Moreover, a machine learning-based parameter adaptation mechanism is proposed to configure DE parameters during the search process. The new adaptive DE has been tested to find the optimal mechanical structure of a novel electric motor topology. Furthermore, the results have been validated using the real-world problems from the CEC 2011 test suite. The results have revealed that the proposal can be competitive compared to recent adaptive DE algorithms.

Optimal camera placement (OCP) is one of many practical applications of a core NP-complete problem in the field of combinatorial optimisation: set cover (SCP). In a generic form, the OCP problem relates to the positioning and setting up of individual cameras such that the overall network is able to cover a given area while meeting a set of application-specific constraints (such as image quality or redundancy) and optimising an objective, typically minimum cost or maximum coverage, depending on the application's focus. In this paper, we consider the problem of positioning and orienting a minimal number of cameras such that the network is able to reach full coverage. More specifically, we introduce a framework for OCP instance generation which leaves the common realm of academic study cases and models the problem in real-world settings, using 8 West-European cities for numerical tests. A baseline is established by running several basic algorithms, which serve as a starting point for a more focused benchmark involving various state-of-the-art algorithms from both OCP and SCP literature. The results are then discussed and several elements highlighted for future research.

Differential Evolution (DE) is a well-known metaheuristic designed to solve continuous optimization problems. Its simple structure and straight forward search operators make it suitable for solving a wide range of real world problems. Despite its success, DE performance may be limited when tackling high dimensional complex problems. Therefore, its algorithmic structure can be reconsidered by adaptively controlling its parameters, and incorporating more resilient search operators. In this study, a Q-learning-based strategy is proposed to adapt DE parameters during the search process. Moreover, an eigenvector-based crossover is introduced in order to accelerate the convergence rate when ill-conditioned landscapes are treated. However, to avoid premature convergence, a simple yet efficient switching technique is proposed to choose between the normal and the eigenvector-based crossover. Due to the high computational time that might occur when applying the eigenvector-based crossover, a parallel counterpart of the algorithm has been implemented using graphics processing units (GPUs). The proposed algorithm has been applied to find the optimal mechanical structure of a recent electric motor. Its performance has been also validated by testing the proposal on CEC 2011 test suite, which contains a set of real world problems. The experimental results reveal the competetive performance of our algorithm compared to recent adaptive DE versions. Besides, the parallel version of the proposal achieved a serious speedup compared with the sequential version while keeping the same results.

The performance of Differential Evolution (DE) algorithm strongly depends on its control parameters. Despite its efficiency and wide use, it might get trapped in local minimum due to premature convergence. In this study, a novel parameter adaptation strategy is proposed to address the mentioned problems. To do so, a pheromone matrix is employed to adjust parameter setting of the algorithm during the optimization process. Moreover, the convergence issue of DE is tackled by incorporating a new restart strategy. The performance of the proposed algorithm is firstly evaluated on the CEC 2011 real world problems test suite. Thereafter, we applied the algorithm to find optimized structure of a recent electric motor design considered for this study. The results reveal the competitive performance of the proposed approach with state-of-the-art algorithms.

The surrogate models are offered as effective tools to approximate computationally expensive objective functions. This study investigates how approximation strategy of such models can be used for high dimensional protein structure prediction (PSP) problems. Two major contributions of the proposed approach are: 1) employing Stochastic Response Surface (SRS) to bias the initial population toward promising areas and 2) using queries of an active learning algorithm and a surrogate model to replace in part the original computationally expensive solver. The introduced framework is applied on several extensions of the differential evolution (DE) algorithm which are among noteworthy approaches for the PSP. Numerical experiments indicate that the proposed schema is able to improve performance of the conventional algorithms for the PSP problems in both terms of convergence speed and accuracy.

The majority of evolutionary algorithms (EAs) adopt Pseudo-Random Numbers Generator (PRNG) to initialize their population. This can affect the behavior of an EA for high dimensional problems due to the curse of dimensionality and has been known as a potentially serious challenge. Therefore, intelligent initialization of individual candidates has been more explored recently. As a different approach, this study proposes a machine-learning based algorithm to address the aforementioned problem. The introduced SVM based Smart Sampling, we call as SVM-SS, employs Support Vector Machine (SVM) to discover promising regions faster. The proposed method and Differential Evolution (DE) are then combined to evaluate our approach. Numerical results on a set of classic benchmark functions show that the proposed algorithm performs better in comparison with several state-of-the-art population initialization methods. To examine the scalability of the SVM-SS, it is also applied on large scale optimization problems and such results were also in consonance with the previous experiments.