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 study scrutinizes a facility location problem with uniformly distributed demands along the network edges. The objective is to determine the best locations for establishing facilities such that the aggregate traveling time is minimized. Each network edge is divided into two segments, each assigned to its closest open facility. Finding the best combination for establishing facilities and using them as a basis for decomposing network edges form the main decision variables. Due to the NP-hardness of this problem, a Genetic Algorithm is used as the optimization method. This algorithm is known as one of the best metaheuristics for solving this problem. To accelerate the optimization process considering the computationally expensive fitness evaluation of the edge-based location problems, an extreme learning machine is hybridized with the implemented genetic algorithm to serve as a surrogate model for approximating the fitness of the majority of individuals. The results obtained from solving generated instances indicate that while keeping the same quality of solutions, the developed surrogate model-based genetic algorithm significantly reduces the required computational time.

This paper presents a novel bi-level optimization approach for addressing the 3D truck loading problem, incorporating considerations of axle weights and items with diverse delivery time windows, as defined in the 2022 EURO/ROADEF Challenge. The main goal is to optimize the efficient stacking and allocation of items into appropriate trucks, to minimize both inventory costs and transportation expenses, which encompass the additional costs associated with employing extra trucks. The first level focuses on optimizing the assignment of items to appropriate trucks, considering factors such as weight limits and compatibility. The second level initiates using an efficient heuristic for generating stacks. Using a combination of a developed Tabu Search algorithm with multiple heuristics, the generated stacks are placed within the assigned trucks such that the unused space is minimized. The computational results demonstrate the advantage of our proposal results compared to the best-known results from the EURO/ROADEF challenge, considering identical system specifications and computational times.

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.

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.

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.