Swarm and Evolutionary Computation最新文献

The COVID-19 pandemic profoundly affects elective surgery and healthcare resources. Efficient management of resources, like ward capacity and operating theaters, is crucial. The operations research community explores solutions, notably leveraging artificial intelligence, to address scheduling challenges amid COVID-19 restrictions. In this situation, applying AI becomes essential to getting the best results. In this paper, we address the problem of daily scheduling elective surgeries while accounting for hospital ward capacity. It is possible to reduce this issue to a scheduling puzzle that, given a variety of restrictions, resembles a four-stage hybrid flow shop. These limitations include the availability of resources, patient flow control, wait time avoidance, patient prioritizing, and resource coordination. With the crucial aid of artificial intelligence, our main goal is to assign patients to different surgical resources to minimize the length of time they spend on average in the hospital ward. We suggest putting into practice effective optimization strategies that make use of AI-based algorithms, particularly the variable neighborhood search (VNS) and variable neighborhood descent (VND) algorithms, which are inextricably linked with artificial intelligence concepts. Our studies demonstrate the effectiveness and efficiency of the general VNS in addressing the daily elective surgical scheduling issue (SSP) with the priceless assistance of artificial intelligence. The experiments are based on novel data instances that were inspired by current literature guidelines. The test results conclusively demonstrate the ability of our algorithms to find virtually perfect solutions. Moreover, our results highlight that the use of these methods, strengthened by AI, can significantly increase the size of the solved issue by a remarkable factor of 19.54. In light of the current COVID-19 pandemic, AI thus becomes a key factor in optimizing the scheduling of elective surgeries and the allocation of resources.

With the increasing reliance on cloud computing as the foundational manufacturing systems with intricate dynamics, featuring multiple service areas, varying job arrival rates, diverse service requirements, and the interplay of failures and impatience, significant analytical challenges arise. Queueing networks offer a powerful stochastic modeling framework to capture such complex dynamics. This paper develops a novel, exhaustive queueing model for a finite-capacity redundant multi-server system operating in a multi-phase random environment. The proposed model uniquely integrates real-world factors, including server breakdowns and repairs, waiting servers, synchronous working vacations, and state dependent balking and reneging, into a single queueing model, representing a significant advancement in the field. Using the matrix-analytic method, we establish the steady-state solution and derive key performance metrics. Numerical experiments and sensitivity analyses elucidate the impact of system parameters on performance measures. Additionally, a cost model is formulated, enabling cost optimization analysis using direct search method and Particle Swarm Optimization (PSO) to identify efficient operating configurations.

Multimodal multiobjective optimization problems (MMOPs) have attracted extensive research interest. These problems are characterized by the presence of multiple equivalent optimal solutions in the decision space, all corresponding to the same optimal values in the objective space. However, effectively finding a high-quality and evenly distributed Pareto sets (PSs) remains a challenge for researchers. This paper introduces a multimodal multiobjective differential evolution algorithm based on enhanced decision space search (MMODE_EDSS). By adopting two types of strategies to enhance the decision space search capability, the algorithm generates multiple high-quality non-dominated solutions. In the early stages of evolution, neighborhood information is used to enhance search capabilities, while in the later stages, data interpolation methods following clustering are employed for searching. Moreover, to improve the overall population distribution, an environmental selection mechanism based on dual-space crowding distance is adopted. The effectiveness of the proposed algorithm, MMODE_EDSS, is evaluated by comparing it with eight state-of-the-art multimodal multiobjective evolutionary algorithms (MMOEAs). Experimental results confirm the significant advantages of MMODE_EDSS.

In recent years, Neural Architecture Search (NAS) has received widespread attention for its remarkable ability to design neural networks. However, existing NAS works have mainly focused on network search, with limited emphasis on downstream applications after discovering efficient neural networks. In this paper, we propose a lightweight search strategy based on the particle swarm optimization algorithm and apply the searched network as backbone for object detection tasks. Specifically, we design a lightweight search space based on Ghostconv modules and improved Mobileblocks, achieving comprehensive exploration within the search space using variable-length encoding strategy. During the search process, to balance network performance and resource consumption, we propose a multi-objective fitness function and incorporated the classification accuracy, parameter size, and FLOPs of candidate individuals into optimization. For particle performance evaluation, we propose a new strategy based on weight sharing and dynamic early stopping, significantly accelerating the search process. Finally, we fine-tune the globally optimal particle decoded as the backbone, adding Ghost PAN feature fusion modules and detection heads to build an object detection model, and we achieve a 17.01% mAP on the VisDrone2019 dataset. Experimental results demonstrate the competitiveness of our algorithm in terms of search time and the balance between accuracy and efficiency, and also confirm the effectiveness of object detection models designed through NAS methods.

Fast and accurate parameter identification of the photovoltaic (PV) model is crucial for calculating, controlling, and managing PV generation systems. Numerous meta-heuristic algorithms have been applied to identify unknown parameters due to the multimodal and nonlinear characteristics of the parameter identification problems. Although many of them can obtain satisfactory results, problems such as premature convergence and population stagnation still exist, influencing the optimization performance. A novel variant of Differential Evolution, namely, Diversity Improvement-Oriented Differential Evolution (DIODE), is proposed to mitigate these deficiencies and obtain reliable parameters for PV models. In DIODE, an adaptive perturbation strategy is employed to perturb current individuals to mitigate premature convergence by enhancing population diversity. Secondly, a diversity improvement mechanism is proposed, where information on the covariance matrix and fitness improvement of individuals is used as a diversity indicator to detect stagnant individuals, which are then updated by the intervention strategy. Lastly, a novel parameter adaptation strategy is employed to maintain a sound balance between exploration and exploitation. The proposed DIODE algorithm is applied to parameter identification problems of six PV models, including single, double, and triple diode and three PV module models. In addition, a large test bed containing 72 benchmark functions from CEC2014, CEC2017, and CEC2022 test suites is employed to verify DIODE’s overall performance in terms of optimization accuracy. Experiment results demonstrate that DIODE can secure accurate parameters of PV models and achieve highly competitive performance on benchmark functions.

The distributed job-shop scheduling problem (DJSP) is an extension of the traditional job-shop scheduling problem, which are composed of two sub-problems, assigning jobs to suitable factories and deciding the operation sequence on machines. To evaluate the performance of algorithms for solving DJSP, several famous benchmark instances have been proposed, and most of these instances have not been solved so far. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for solving DJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. In GA part, a crossover operation and a mutation operation are devised based on the critical factory. The two operations can effectively improve the discreteness of the population. In TS part, a tabu search procedure is performed on the critical factory. The procedure can effectively enhance the local search ability of HGTSA. For evaluating the performance of HGTSA, it has been compared with five classical algorithms on 240 benchmark instances. The computational results show the efficiency and effectiveness of HGTSA for solving DJSP. In particular, the proposed HGTSA updates the upper bounds for 235 out of these difficult instances.

Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms.

The distributed heterogeneous mixed scheduling mode in the manufacturing systems emphasizes the cooperation between factories for the entire production cycle, which poses enormous challenges to the processing and assignment of jobs. Discrepancies in the processing environment and types of machines of each factory during various production stages cause diverse processing paths and scheduling. The distributed heterogeneous mixed no-wait flow-shop scheduling problem with sequence-dependent setup time (DHMNWFSP-SDST), abstracted from the industrial scenarios, is addressed in this paper. The mathematical model of DHMNWFSP-SDST is established. A cooperative learning-aware dynamic hierarchical hyper-heuristic (CLDHH) is proposed to solve the DHMNWFSP-SDST. In CLDHH, a cooperative initialization method is developed to promote diversity and quality of solutions. A hierarchical hyper-heuristic framework with reinforcement learning (RL) is designed to select the algorithm component automatically. Estimation of Distribution Algorithm (EDA) guides the upper-layer RL to select four neighborhood structures. A dynamic adaptive neighborhood switching constructs the lower-layer RL to accelerate exploitation with the dominant sub-neighborhoods. An elite-guided hybrid path relinking achieves local enhancement. The experimental results of CLDHH and six state-of-the-art algorithms on instances indicate that the proposed CLDHH is superior to the state-of-the-art algorithms in solution quality, robustness, and efficiency.

This paper focuses on the bi-objective path planning problem of multiple unmanned aerial vehicles (UAVs) under the complex environment with numerous obstacles and threat areas, where the UAVs need to be kept as far away as possible from threat areas during flight. Based on the integrated energy reduction perspective, a bi-objective model is subtly constructed by minimizing the total energy consumption of each path (including flight altitude, horizontal turns, and path length), and minimizing the costs of the total threats (including ground radar, anti-aircraft gun, missile and geological hazard threat areas). Moreover, a two-stage knowledge-assisted coevolutionary NSGA-II algorithm is novelly proposed to enhance collaboration and avoid collision. The first stage is designed for population convergence, where the considered constrained problem is solved with the help of the designed problem without the constraints of threats and obstacles. The second stage emphasizes the quality and diversity of solutions. In this stage, a double-population coevolution approach is developed. Additionally, a multi-mode strategy is introduced for the inferior population, leveraging reinforcement learning. This strategy aids in selecting the optimal mode from random swing, directed guidance, and potential dominance exploration. Furthermore, experimental results in two different environments show that the proposed algorithm can better solve the collaborative path planning problem for multiple UAVs compared with other five classical or recent proposed algorithms.

In many-objective optimization, both convergence and diversity are equally important. However, in high-dimensional spaces, traditional decomposition-based many-objective evolutionary algorithms struggle to ensure population diversity. Conversely, traditional Pareto dominance-based many-objective evolutionary algorithms face challenges in ensuring population convergence. In this paper, we propose a novel many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism (MaOEAIH) for effectively addressing the difficulty in balancing convergence and diversity. First, we use the concept of interaction force to simulate the convergence (akin to gravity) and diversity (repulsion) of the population. Subsequently, we design an optimization mechanism that combines decomposition and Pareto dominance to enhance the convergence and diversity of the population separately. Simultaneously, to eliminate dominance resistance solutions, we propose a quartile method based on boundary solutions. Additionally, Random perturbations are also introduced to certain individuals within the population to facilitate their escape from local optima. MaOEAIH is compared with some state-of-the-art algorithms on 31 well-known test problems with 3-15 objectives. The experimental results show that, compared to other algorithms, MaOEAIH not only obtains solution sets of higher quality when dealing with different types of many-objective optimization problems, but also effectively addresses key challenges including insufficient selection pressure, difficulty balancing convergence and diversity, and susceptibility to population entrapment in local optima within many-objective optimization scenarios.