Adaptive Neighborhood Search Research Articles

Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem

The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic algorithms, such as adaptive neighborhood search (ANS) and variable neighborhood descent (VND), mainly focus on optimizing the current solution without considering potential long-term consequences, which may easily get stuck in local optima and limit the overall performance. Thus, we propose a multi-agent deep Q-network-based metaheuristic algorithm (MDQN-MA) for NRP to harness the strengths of various metaheuristics. Each agent encapsulates a metaheuristic algorithm, where its available actions represent different perspectives of the problem environment. By combining their strengths and various perspectives, these agents can work collaboratively to navigate and search for a broader range of potential solutions effectively. Furthermore, to improve the performance of an individual agent, we model its neighborhood search as a Markov Decision Process model and integrate a deep Q-network to consider long-term impacts for its neighborhood sequential decision-making. The experimental results clearly show that an individual agent in MDQN-MA can outperform ANS and VND, and multiple agents in MDQN-MA even perform better, achieving the best results among metaheuristic algorithms on the Second International Nurse Rostering Competition dataset.

Robot dispatching problem for compact storage and retrieval systems with dual-command transactions

A robotic compact storage and retrieval system (RCSRS) is a grid-based system that has been popularly implemented by online retailers around the world recently. This article addresses a robot dispatching problem for RCSRS, aiming to reduce robot empty travelling time and workstation idle time. The robot dispatching problem is to assign tasks to robots when the task sequences in workstations are given. Additionally, the robots follow a fast dual-command transaction process in this problem. Hence, a mixed integer programming model catering for the dual-command transaction process is formulated to optimize system performance. Furthermore, an adaptive neighbourhood search with an ε-greedy algorithm is proposed to solve the problem. The experimental results substantiate the superiority of the authors' proposed algorithm compared to various algorithms used in practice and discussed in the relevant literature. Moreover, optimal system configurations for the instances generated are suggested.

Inverse model and adaptive neighborhood search based cooperative optimizer for energy-efficient distributed flexible job shop scheduling

Solving the energy-efficient distributed flexible job shop scheduling problem (EEDFJSP) obtains increased attention. However, most previous studies barely considered the large-scale nature of the decision variables of EEDFJSP. In this paper, the large-scale EEDFJSP with two minimized objectives of makespan and total energy consumption (TEC) is studied. To efficiently deal with this problem, an inverse model and adaptive neighborhood search based cooperative optimizer is proposed. First, the inverse model is applied to the job shop scheduling problem. Then, the inverse model and adaptive local search operators cooperate search is designed to obtain offspring. Furthermore, an adaptive strategy for local search operators is developed. Finally, it is compared with other multi-objective optimization algorithms to confirm the effectiveness of the proposed algorithm, including MOEA/D, NSGA-II, IM-MOEA/D, HMMA, HSLFA, and SPAMA. Experimental results demonstrate the superior performance in solving EEDFJSP compared to these six state-of-the-art multi-objective optimization algorithms.

An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling

The type-2 multi-objective integrated process planning and scheduling problem, as an NP-hard problem, is required to deal with both process planning and job shop scheduling, and to generate optimal schedules while planning optimal machining paths for the workpieces. For the type-2 multi-objective integrated process planning and scheduling problem, a mathematical model with the minimization objectives of makespan, total machine load, and critical machine load is developed. A multi-objective mayfly optimization algorithm with decomposition and adaptive neighborhood search is designed to solve this problem. The algorithm uses two forms of encoding, a transformation scheme designed to allow the two codes to switch between each other during evolution, and a hybrid population initialization strategy designed to improve the quality of the initial solution while taking into account diversity. In addition, an adaptive neighborhood search cycle based on the average distance of the Pareto optimal set to the ideal point is designed to improve the algorithm’s merit-seeking ability while maintaining the diversity of the population. The proposed encoding and decoding scheme can better transform the continuous optimization algorithm to apply to the combinatorial optimization problem. Finally, it is experimentally verified that the proposed algorithm achieves better experimental results and can effectively deal with type-2 MOIPPS.

Multi-objective fuzzy partial disassembly line balancing considering preventive maintenance scenarios using enhanced hybrid artificial bee colony algorithm

In the existing researches about partial disassembly line balancing problem (PDLBP), all workstations are assumed always be available. In the real world, however, aging and failure of machines are inevitable, which may result in the shutdown of the whole disassembly line. To solve this problem, this study introduces preventive maintenance scenarios into PDLBP to prevent unexpected breakdowns and promote the productivity and smoothness of the disassembly line. Then, considering the substantial level of uncertainty in the state of end-of-life products, a multi-objective fuzzy mathematical model is established to minimize the cycle time, disassembly profit, and total assignment plan alteration simultaneously. And an enhanced hybrid artificial bee colony algorithm is developed to solve it. The attribute-driven heuristic strategy is applied to improve the quality of initial food sources, and the adaptive neighborhood search is designed to improve the exploitation efficiency of the employed bees and onlooker bees. Additionally, a hybrid global learning strategy is developed to guide the scout bees to raise the diversity of food sources. Finally, three case studies are conducted to validate the proposed algorithm. The results indicate that the proposed algorithm can achieve superior performance.

Adaptive evidential K-NN classification: Integrating neighborhood search and feature weighting

The number of nearest neighbors K and the utilized distance measure considerably impact the performance of the K-nearest neighbor (K-NN) algorithm. The information provided by neighbors may be perplexing due to the set presumption of K for every test sample without any prior knowledge, which might result in incorrect classification results. The hypothesis of locally constant class conditional probabilities relied on by K-NN, is violated by high dimensions, which also bring out the dimensionality curse. Notably, in the case of imperfect data, training samples may be noise-corrupted or located in substantially overlapping regions, thus impairing the effectiveness of the K-NN rule. In contrast to existing research, which frequently focuses on just one of the problems above, this paper presents an adaptive evidential K-NN classification (AEK-NN) algorithm that synchronizes neighborhood search with feature weighting. In order to jointly learn the adaptive neighborhood for each sample and various feature weights, AEK-NN maps the Euclidean space into a rebuilt space consisting of similarity coefficients through a two-stage training process. Within the evidence theory framework, AEK-NN produces classification results for data with imperfect labels. The advantages of applying evidence theory, incorporating adaptive neighborhood search, and feature weighting are shown through ablation studies. We also use simulated and real-world datasets to demonstrate the state-of-the-art performance of AEK-NN.

A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems

Multi-objective evolutionary algorithm based on decomposition has been well-recognized in addressing bi-objective optimization problems. However, it is a serious challenge for MOEA/D to optimize real-world complicated bi-objective optimization problems which have irregular optimal objective spaces, such as long-tailed, peaked, and disconnected types. Hence, an improved MOEA/D with global and local cooperative mechanisms is proposed in this paper for complicated bi-objective optimization problems. The overall optimization is carried out by the coordination of global and local search phases. Specifically, a bidirectional global search strategy based on reference points is first used to broaden the search regions. Aiming at the irregularity of the Pareto fronts, an adaptive neighborhood search strategy is proposed according to the individual sparsity, which can further explore near the obtained global optimal solutions. The cooperation of these two strategies is designed to improve the distribution and uniformity of the population on complicated bi-objective optimization problems. The performance of the proposed algorithm is investigated on a representative set of benchmark test functions and a real-world water resource allocation problem. The simulation results show that the proposed algorithm is very promising for complicated bi-objective optimization problems.

Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach

For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629.

Optimization of cooperative offloading model with cost consideration in mobile edge computing

The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications and integrating collaborative computing techniques into edge computing. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.); therefore, we propose a cost-based collaborative computation offloading model. In this model, when a task requests these devices’ assistance in computing, it needs to pay the corresponding calculation cost; and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing

With the growing demand for speed and flexibility in order fulfillment, it is crucial to employ an efficient picking system that allows faster delivery of items from warehouse storage to a depot. The use of autonomous mobile robots (AMRs) for intra-logistics can improve picking productivity, while alleviating the strain on human workers. In this research, a collaborative human–robot order-picking system (CHR-OPS) is considered, where humans perform item retrieval tasks and AMRs handle item transportation to the depot. The delivery performance of a CHR-OPS for a given set of orders and their due dates depend on three subproblems: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts system employing multiple pickers and AMRs. An optimization model is developed to jointly optimize the three problems with the objective of minimizing the total tardiness of all orders. A simulated annealing algorithm with adaptive neighborhood search and optimization-based restart strategy is proposed for handling large instances. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human–robot team composition, AMR speed, AMR capacity and warehouse layout.

A Chaotic Genetic Algorithm with Variable Neighborhood Search for Solving Time-Dependent Green VRPTW with Fuzzy Demand

Aiming at the time-dependent green vehicle routing problem with fuzzy demand, this paper comprehensively considers the dispatch costs, time window penalty costs, fuel costs, and the effects of vehicle travel speed, road gradient, and vehicle load on fuel consumption, a mixed integer programming model is formulated based on pre-optimization and re-optimization strategies. The traditional vehicle routing problems are modeled based on a symmetric graph. In this paper, considering the influence of time-dependent networks on route optimization, modeling is based on an asymmetric graph, which increases the complexity of the problem. In the pre-optimization stage, a pre-optimization scheme is generated based on the credibility measure theory; in the re-optimization stage, a new re-optimization strategy was used to deal with the service failure node In order to solve this problem, we developed a chaotic genetic algorithm with variable neighborhood search, pseudo-randomness of chaos was introduced to ensure the diversity of initial solutions, and adaptive neighborhood search times strategy and inferior solution acceptance mechanism were proposed to improve the performance of the algorithm. The numerical results show that the model and algorithm we proposed are effective.

A Two-Stage Hybrid Heuristic Algorithm for Simultaneous Order and Rack Assignment Problems

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently.

A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm

The classical distributed flexible job shop scheduling problem (DFJSP) mainly considers factory allocation, machine arrangement, job sequencing and transportation. To date, the relevant literature has not studied the DFJSPs with worker arrangement, which widely exists in practical manufacturing systems. In this paper, we investigate the DFJSP with worker arrangement (DFJSPW), where not only the factories, machines and operations, but the workers are considered simultaneously. A mixed-integer linear programming model is formulated for this problem. Correspondingly, an improved memetic algorithm (IMA) based on the structure of NSGA-II is proposed for the proposed DFJSPW whose objective is to minimize the makespan, maximum workload of machines and workload of workers simultaneously. In IMA, a simplified two-level encoding and four heuristic decoding methods are designed to encode and decode the individuals. A well-designed adaptive neighborhood search operator is developed to enhance the local search ability of IMA and speed its convergence. Fifty-eight benchmarks are constructed to evaluate the performance of our proposed IMA. Extensive experiments show that in most examples, IMA performs better than four well-known multi-objective algorithms, demonstrating the superiority of IMA in solving the DFJSPW.

A fuzzy correlation based heuristic for Dual-mode integrated Location routing problem

This paper focuses on a variant location routing problem (LRP) named Dual-mode integrated Location Routing Problem (DMI-LRP) in modern supply logistics system. The warehouses in DMI-LRP supply the demands of retailers and online-shopping customers simultaneously and the retailers and customers follow two different order cycles. The problem is presented in a two-echelon structure where products are transported from the plant to the distribution centers (DCs) in the first echelon, and from the DCs to retailers and customers in the second echelon. The mathematical formulation is given together with some valid inequalities to strengthen the model for exact method. We propose the fuzzy correlation (FC) based location for DCs and allocation for retailers and customers in LRP. Also, a fuzzy correlation arc based adaptive neighborhood search (FCA-AVNS) algorithm is designed for path planning. The proposed method is tested on three adapted benchmark instances and the results are compared with exact method and another three heuristics. The numerical experiments demonstrate the feasibility and efficiency of the proposed method especially in solving large-scale problems.

Improved Multi-Verse Optimizer Feature Selection Technique With Application To Phishing, Spam, and Denial Of Service Attacks

Intelligent classification systems proved their merits in different fields including cybersecurity. However, most cybercrime issues are characterized of being dynamic and not static classification problems where the set of discriminative features keep changing with time. This indeed requires revising the cybercrime classification system and pick a group of features that preserve or enhance its performance. Not only this but also the system compactness is regarded as an important factor to judge on the capability of any classification system where cybercrime classification systems are not an exception. The current research proposes an improved feature selection algorithm that is inspired from the well-known multi-verse optimizer (MVO) algorithm. Such an algorithm is then applied to 3 different cybercrime classification problems namely phishing websites, spam, and denial of service attacks. MVO is a population-based approach which stimulates a well-known theory in physics namely multi-verse theory. MVO uses the black and white holes principles for exploration, and wormholes principle for exploitation. A roulette selection schema is used for scientifically modeling the principles of white hole and black hole in exploration phase, which bias to the good solutions, in this case the solutions will be moved toward the best solution and probably to lose the diversity, other solutions may contain important information but didn’t get chance to be improved. Thus, this research will improve the exploration of the MVO by introducing the adaptive neighborhood search operations in updating the MVO solutions. The classification phase has been done using a classifier to evaluate the results and to validate the selected features. Empirical outcomes confirmed that the improved MVO (IMVO) algorithm is capable to enhance the search capability of MVO, and outperform other algorithm involved in comparison.

Adaptive deep learning-based neighborhood search method for point cloud

Point cloud processing is a highly challenging task in 3D vision because it is unstructured and unordered. Recently, deep learning has been proven to be quite successful in point cloud recognition, registration, segmentation, etc. Neighborhood search operation is an important component of point cloud deep learning models, and directly affects the performance of the model. In this paper, we propose a learnable neighborhood search method. This method adaptively chooses an appropriate search method based on the characteristics of each point, thus avoiding the disadvantage of selecting the search method manually. We validate the proposed methods on ModelNet40 dataset and ShapeNetPart dataset, and all the chosen models achieved a performance improvement with a maximum improvement of 1.1%. The proposed method is a plug-and-play technique and can be easily integrated into existing methods.

Research on Effective Path Planning Algorithm Based on Improved A* Algorithm

In recent years, with the rapid development of technology, mobile robots are being applied in all aspects of production and life. The A* algorithm can be used for global path planning of mobile robots. Aimed at the problem of the traditional A* algorithm planning path having many turning points and do not satisfying the global optimality, an A* algorithm based on the adaptive neighborhood search and steering cost has been proposed. Based on the information of the surrounding obstacles, the algorithm adaptively selects the appropriate neighborhood to search for the optimal child node. By establishing the steering cost model of the mobile robot, the steering cost is joined into the evaluation function of the A* algorithm. Based on the adaptive neighborhood search, the A* algorithm with steering cost breaks through the constraint of eight neighborhood search nodes, shortens the path length, and uses the steering cost to search for the global optimal path with fewer turning points. The simulation results show that compared to the traditional A* algorithm, the total path cost of the A* algorithm based on adaptive neighborhood search and steering cost is reduced by 19.3%, and the number of turning points is reduced by 44.4%.

Research on collaborative optimisation of urban agricultural product distribution centre location and routing based on improved adaptive large-scale neighbourhood search algorithm

Research on collaborative optimisation of urban agricultural product distribution centre location and routing based on improved adaptive large-scale neighbourhood search algorithm

Research on collaborative optimisation of urban agricultural product distribution centre location and routing based on improved adaptive large-scale neighbourhood search algorithm

Research on collaborative optimisation of urban agricultural product distribution centre location and routing based on improved adaptive large-scale neighbourhood search algorithm

Research on LRP Integration of E-Commerce Logistics under the Background of Integration of Collection and Distribution

The distribution network composed of location and route is an important part of e-commerce logistics. With the continuous improvement of e-commerce requirements for logistics level, the practice of planning logistics network only from the perspective of the network location or the vehicle route can no longer meet the actual demand. In addition to the comprehensive consideration of the location-routing problem, the reverse logistics caused by customers’ returning goods should be taken into account. In this paper, the destruction and reorganization strategy of adaptive large-scale neighborhood search algorithm was introduced into the traditional genetic algorithm, so as to conduct research on the logistics location-routing problem under the background of integration of collection and distribution. Finally, the effectiveness of the optimized genetic algorithm was verified by Matlab tools and the existing bench-marking data set of the location-routing problem, which provided reference for the planning and decision-making of logistics enterprises.