Late Acceptance Hill-climbing Algorithm Research Articles

Dynamic mutation late acceptance hill climbing aided red fox optimization for metabolomic biomarkers selection from lung cancer patient sera

The accurate selection of serum metabolomic biomarkers for early lung cancer screening remains a significant challenge in the clinical context. Consequently, this study introduces the Red Fox Optimization (RFO) that integrates Dynamic Mutation Late Acceptance Hill Climbing (DM-LAHC), with the aim of selecting a panel of serum metabolomic biomarkers suitable for distinguishing between benign and malignant pulmonary nodules. The key innovation is the dynamic adjustment of the mutation probability in the Late Acceptance Hill Climbing algorithm, which greatly enhances the local search capabilities. And the RFO's reproduction mechanism has been improved through the utilization of a more efficient interpolation form. The biomarker selection model employs a multi-objective fitness function that takes into account both accuracy and quantity. After this, the optimal model yielded a biomarker panel, including Inosine, Hippuric acid, Alanine, and other metabolites. This model demonstrates outstanding performance on an independent test dataset, achieving a fitness value of 0.9136, an AUC (Area Under the Curve) of 0.9926, a sensitivity of 0.9643, and a specificity of 0.9412. Furthermore, the clinical net benefit is highlighted across various risk thresholds by decision curve analysis. These results underscore the significance of DM-LAHC aided RFO in the selection of serum metabolomic biomarkers for lung cancer. The supporting source codes of this work can be found at: https://github.com/zzl2022/DM-LAHC-aided-RFO.

Research of a Multi-Level Organization Human Resource Network Optimization Model and an Improved Late Acceptance Hill Climbing Algorithm

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.

Type-1 Robotic Assembly Line Balancing Problem That Considers Energy Consumption and Cross-Station Design

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The “cross-station task” design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm.

Path Planning for Indoor UAV Using A* and Late Acceptance Hill Climbing Algorithms Utilizing Probabilistic Roadmap

The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles. Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm.

Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human–robot collaboration

The collaboration of human workers and robots draws increasing attention from the manufacturing enterprises to embrace the Industry 4.0 paradigm in a competitive way. Motivated by the requirements of collaboration between human workers and robots in assembly lines, this study investigates the mixed-model assembly line balancing (MMALB) problem with the collaboration between human workers and robots. A mixed-integer linear programming (MILP) model is formulated to tackle the small-size problems optimally to minimize the sum of cycle times of models. Also, bee algorithm (BA) and artificial bee colony (ABC) algorithm are implemented and improved to solve the large-size problems due to the NP-hardness of this problem. The proposed BA algorithm utilizes a new employed bee phase to accelerate the evolution of the swarm and new scout phase to escape from being trapped into local optima and produce a high-quality and diverse population. The developed ABC proposes a new onlooker phase to accelerate the evolution of the whole swarm by removing the poor-quality solutions, new scout phase to achieve high-quality solutions while preserving the diversity of the swarm, and local search to enhance exploitation capacity. Computational study on a set of generated instances indicates that the improvements enhance the BA and ABC algorithm by a significant margin, and the proposed BA and ABC algorithm achieve competing performance in comparison with nine other algorithms, including the late acceptance hill-climbing algorithm, simulated annealing algorithm, genetic algorithm, particle swarm optimization algorithm, discrete cuckoo search algorithm, the original bee algorithm, and three artificial bee colony algorithms.

A cutoff time strategy based on the coupon collector’s problem

Throughout the course of an optimization run, the probability of yielding further improvement becomes smaller as the search proceeds, and eventually the search stagnates. Under such a state, letting the algorithm continue to run is a waste of time as there is little hope that subsequent improvement can be made. The ability to detect the stagnation point is therefore of prime importance. If such a point can be detected reliably, then it is possible to make better use of the computing resources, perhaps restarting the algorithm at the stagnation point, either with the same or with a different parameter configuration.This paper proposes a cutoff time strategy. It presents a method that is able to reliably detect the stagnation point for one-point stochastic local search algorithms applied to combinatorial optimization problems. The strategy is derived from the coupon collector’s problem, and is scalable based on the employed perturbation operator and its induced neighbourhood size, as well as from feedback from the search. The suitability and scalability of the method is illustrated with the Late Acceptance Hill-Climbing algorithm on a comprehensive set of benchmark instances of three well-known combinatorial optimization problems: the Travelling Salesman Problem, the Quadratic Assignment Problem, and the Permutation Flowshop Scheduling Problem.

A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.

Electronic health records (EHR) are a major source of information in biomedical informatics. Yet, missing values are prominent characteristics of EHR. Prediction on dataset with missing values results in inaccurate inferences. Nearest neighbour imputation based on lazy learning approach is a proven technique for missing data imputation and is recognized as one among the top ten data mining algorithms due to its simplicity and understandability. But its performance is deteriorated due to the curse of dimensionality as unimportant features are likely to dominate. We address this problem by proposing a novel approach for feature weighting based on a hybrid of metaheuristic whale optimization algorithm (WOA) and local search late acceptance hill climbing algorithm (LAHCA) on nearest neighbour imputation method. Our proposed approach Metaheuristic and Local Search based Feature Weighted Nearest Neighbour Imputation (kNN+LAHCAWOA) also learns different k values for different test points. Our approach is tested on benchmark EHR datasets with three proven classifiers Support Vector Machines(SVM), Random forest(RF) and Deep neural networks(DNN). The results prove that kNN+LAHCAWOA is an effective imputation strategy and aids in improving the classification performance when compared with its competitor methods.

Late Acceptance Hill Climbing Algorithm For Solving Patient Facing Problems In Hospitals

The current study is to evaluate the patient facing problems by applying Late Acceptance Hill Climbing Algorithm (LAHC) in hospital settings. The recent proposed procedure of LAHC is based on metaheuristic algorithm which is linked with one-point clarification method. Patient’s satisfaction regarding the performance of the hospital is a composite mechanism. The optimization procedure is connected with NP-hard problems which is practically related to the problems faced by patients. These problems are concerned with assigning the group of patients visiting the hospital for receiving healthcare services. The common issues faced by patients include communication gap, response time to attend patients, early symptomatic relief, getting proper advice for dosage and usage of medicines, clean hospital environment. Moreover, patient education and guidance before discharge from hospital is also the missing element. The suggested algorithm of LAHC to PFP is developed and it has two phases: the first phase includes providing the initial feasible solution using communication-oriented methodology. The second phase uses three neighborhood framework which are implanted inside the segment of PFP based on LAHC to additionally upgrade the underlying feasible solution of the introductory phase.

A multiobjective hybrid imperialist competitive algorithm for multirobot cooperative assembly line balancing problems with energy awareness

The environmental and economic pressures caused by energy consumption arouse energy-saving consciousness of the manufacturing industry. In recent years, robots have been extensively used in assembly systems as called robotic assembly lines where the energy consumption is a major expense, particularly in the workstation with the multirobot cooperative assembly of multirobot. To deal with this problem, the paper presents a novel mathematical model with three objectives of minimizing the cycle time, the sum of energy consumption, and the total cost of robots of assembly lines. Due to the nondeterministic polynomial time nature of the considered problem, a multiobjective hybrid imperialist competitive algorithm with nondominated sorting strategy is developed, which uses a representation technique of three-level coding, i.e. the station level, the task level, and the robot level and proposes an original concept of workstation decision assignment matrix to identify the performed tasks by the same type of robots in a workstation. Furthermore, a late-acceptance hill-climbing algorithm is combined into the algorithm to improve the performance of the proposed algorithm. Finally, testing cases are designed to measure the performance of the proposed method by comparing with two other high-performing multiobjective methods. The computational and statistical results show that the proposed multiobjective hybrid imperialist competitive algorithm is conducive to improve the line efficiency, to reduce the sum of energy consumption, and to cut the total cost of robots in an assembly line effectively.

Late acceptance hill climbing algorithm for solving patient admission scheduling problem

This article tackles a patient admission scheduling using Late Acceptance Hill Climbing Algorithm (LAHC). The LAHC algorithm is among the newly proposed metaheuristic-based algorithm which belongs to the one-point solution technique. Patient admission scheduling is a complex combinatorial optimization problem which have been proven to belongs to class of NP-hard problem in almost all its variations. This problem is concerns with assigning a set of patients arriving for medical services in the hospital to a set of rooms, timeslots and beds subject to satisfying a set of predefined constraints. The proposed adaptation of LAHC to the patient admission scheduling named LAHC-based PAS comes in two stages: the first stage involves generating of initial feasible solution using the room oriented-based approach (ROP), while the second stage utilizes three neighbourhood structures which are embedded within the component of LAHC-based PAS to further enhances the initial solution generated at the initial stage. The proposed LAHC method is evaluated using the standard benchmark dataset. The experimental results shows that the proposed method is an effective technique for tackling the PAS problem. It is observed that the method outperform many of the existing techniques when compared with the state-of-the-arts.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Two-sided assembly line balancing with operator number and task constraints: a hybrid imperialist competitive algorithm

Two-sided assembly line balancing problems usually occur in plants producing large-sized high-volume products such as buses, trucks, or cars. Comparing to traditional simple assembly lines, the two-sided assembly line can shorten the length of line, enhance the utilization of tools and fixtures, and improve the production rate of operators, etc. Whereas in a two-sided assembly line, due to a couple of constraints such as the operation direction constraint and the sequence-dependent finish time constraint, the balancing procedure is much more complex than for a simple assembly line. In this paper, besides some fundamental constraints of the two-sided assembly lines, we put forward additionally the restriction of operator number at each workstation, and take into account concurrently more constraints such as the positional constraints, zoning constraints, and the synchronous task constraints. A novel hybrid imperialist competitive algorithm (ICA) combined with the late acceptance hill-climbing (LAHC) algorithm is employed to solve the multiconstraint problem. For the evaluation of the proposed algorithm, the performance of the hybrid ICA is examined over benchmarks from the literature and compared with the LAHC algorithm and the lower bounds of the instances. Furthermore, a practical two-sided assembly line balancing problem from an engineering machinery company is solved with the proposed approach. The results confirm that the hybrid ICA can effectively shorten the line length and reduce the amount of operators for the existing two-sided assembly line.

An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling

The artificial bee colony (ABC) is a population-based metaheuristic that mimics the foraging behaviour of honeybees in order to produce high-quality solutions for optimisation problems. The ABC algorithm combines both exploration and exploitation processes. In the exploration process, the worker bees are responsible for selecting a random solution and applying it to a random neighbourhood structure, while the onlooker bees are responsible for choosing a food source based on a selection strategy. In this paper, a disruptive selection strategy is applied within the ABC algorithm in order to improve the diversity of the population and prevent premature convergence in the evolutionary process. A self-adaptive strategy for selecting neighbourhood structures is added to further enhance the local intensification capability (adaptively choosing the neighbourhood structure helps the algorithm to escape local optima). Finally, a modified ABC algorithm is hybridised with a local search algorithm, i.e. the late-acceptance hill-climbing algorithm, to quickly descend to a good-quality solution. The experiments show that the ABC algorithm with the disruptive selection strategy outperforms the original ABC algorithm. The hybridised ABC algorithm also outperforms the lone ABC algorithm when tested on examination timetabling problems.

A late acceptance hill-climbing algorithm for balancing two-sided assembly lines with multiple constraints

Two-sided assembly lines are widely applied to produce the large-sized high-volume products, such as buses and trucks. Balancing the lines is a vital design problem for the industries, and the problem is NP-hard. Besides the fundamental constraints of the conventional line balancing problem, some specific constraints may occur in the two-sided assembly line problem, including the zoning constraints, the positional constraints, and the synchronism constraints, which make the problem more complex. In this paper, an integer programming (IP) model is constructed and solved for the two-sided assembly line balancing problem which contains the above three constraints. A novel metaheuristic named late acceptance hill-climbing (LAHC) is also proposed to solve the problem effectively. The proposed algorithm is tested on several sets of instances. The computational results of the LAHC algorithm are compared with those of IP and the lower bounds of the instances. The experiment validates the effectiveness of the LAHC algorithm.