Computers & Operations Research, Journal Year: 2025, Volume and Issue: unknown, P. 107034 - 107034
Published: March 1, 2025
Language: Английский
Citations
0Machine-learning-based hyper-heuristics for solving the Knapsack Problem
Pattern Recognition Letters, Journal Year: 2025, Volume and Issue: unknown
Published: March 1, 2025
Language: Английский
Citations
0Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review
Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 34(1)
Published: Jan. 1, 2025
Abstract The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling resource allocation, particularly when optimizing multiple objectives a dynamic environment. Efficient allocation critical computing, as they directly impact system performance, utilization, cost efficiency heterogeneous conditions. Existing approaches often face difficulties balancing conflicting objectives, such reducing task completion time while staying within budget constraints or minimizing energy consumption maximizing utilization. As result, many solutions fall short optimal leading increased costs degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 2023 Following preferred reporting items for reviews meta-analyses guidelines, ensures transparent replicable process by employing inclusion criteria bias. explores key concepts management classifies existing strategies into mathematical, heuristic, hyper-heuristic approaches. It evaluates popular algorithms designed optimize metrics consumption, reduction, makespan minimization, performance satisfaction. Through comparative analysis, SLR discusses strengths limitations various schemes identifies emerging trends. underscores steady growth field, emphasizing importance developing efficient address complexities modern systems. findings provide comprehensive overview current methodologies pave way future aimed at tackling unresolved challenges management. work serves valuable practitioners academics seeking environments, contributing advancements computing.
Language: Английский
Citations
0XAI for Algorithm Configuration and Selection
Natural computing series, Journal Year: 2025, Volume and Issue: unknown, P. 117 - 148
Published: Jan. 1, 2025
Language: Английский
Citations
0Mathematical modeling and optimization of multi-period fourth-party logistics network design problems with customer satisfaction-sensitive demand
Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127219 - 127219
Published: March 1, 2025
Language: Английский
Citations
0Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
Mathematics, Journal Year: 2024, Volume and Issue: 12(8), P. 1249 - 1249
Published: April 20, 2024
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing hybrid model that combines the Orca predator algorithm with deep Q-learning. The is an technique mimics hunting behavior of orcas. It solves complex problems exploring and exploiting search spaces efficiently. Deep Q-learning reinforcement learning neural networks. integration aims turn stagnation problem into opportunity for more focused effective exploitation, enhancing technique’s performance accuracy. proposed leverages biomimetic strengths identify promising regions nearby space, complemented fine-tuning capabilities navigate these areas precisely. practical application approach evaluated using high-dimensional Heartbeat Categorization Dataset, focusing on feature selection problem. dataset, comprising electrocardiogram signals, provided robust platform testing our model. Our experimental results are encouraging, showcasing strategy’s capability relevant features without significantly compromising metrics machine models. analysis was performed comparing improved method against its native version set state-of-the-art algorithms.
Language: Английский
Citations
2A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer
Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 516 - 516
Published: Aug. 27, 2024
Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on genetic (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. can be regarded as multi-objective fuzzy collaborative problem. effectively avoid extensive evaluation repair infeasible solutions during iterative process, which is common issue meta-heuristic algorithms. The GA-SLHH employs combined strategy its high-level (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic rules solution support. Multiple sets numerical experiments demonstrate that exhibits stronger comprehensive ability stability when this Finally, validity addressing real-world issues ship manufacturing companies validated through practical enterprise cases. results case show scheme solved using proposed reduce transportation time by up 37%.
Language: Английский
Citations
2A New Clustering-based View Planning Method for Building Inspection with Drone
IEEE Robotics and Automation Letters, Journal Year: 2024, Volume and Issue: 9(11), P. 9781 - 9788
Published: Aug. 8, 2024
Language: Английский
Citations
1A Q-learning based hyper-heuristic scheduling algorithm with multi-rule selection for sub-assembly in shipbuilding
Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110567 - 110567
Published: Sept. 1, 2024
Language: Английский
Citations
1A Metaheuristic Approach for a Two-dimensional Fuzzy Version of the Variable Size and Cost Bin Packing Problem
International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)
Published: Nov. 18, 2024
The Variable Size and Cost Bin Packing Problem (VSCBPP) focuses on minimizing the overall cost of containers used to pack a specified set items. This problem has significant applications across various fields, including energy, cargo transport, informatics, among others. Most research conducted this concentrated enhancing solution methodologies. Recently, some studies have investigated use fuzzy approaches VSCBPP, which allow for relaxation certain constraints. In paper, we introduce metaheuristic method solving version facilitating simultaneous two constraints: overloading exclusion specific items from packing process. Consequently, two-dimensional VSCBPP enables us derive range solutions that present varying trade-offs between satisfaction levels original We employ mechanisms multi-objective approach maximize degrees while function. To demonstrate efficacy our proposed solution, utilized well-known evolutionary P-metaheuristics (Multi-Objective Genetic Algorithm NSGA-II) S-metaheuristics Local Search Ulungu Multi-Objective Simulated Annealing) specifically tailored VSCBPP. Computational experiments were 39 instances validate effectiveness approach.
Language: Английский
Citations
1