Improved Simulated Annealing Algorithm Solving for 0/1 Knapsack Problem

Liu, Aizhen; Wang, Jiazhen; Han, Guodong; Wang, Shuzhen; Wen, Jia

doi:10.1109/isda.2006.253776

Cited by 25 publications

(19 citation statements)

References 2 publications

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“…When the forecasted information is available, i.e., network slice requests accurately reshaped, the admission control might fit more network slice requests while still guaranteeing the committed traffic SLAs. To this aim, we need to propose a new algorithm that reflects the concept of simulated annealing [29]. The additional complexity is due to the feasibility check of a given set of items into the system capacity: packing items in a different order might influence the solution optimality in the next attempts.…”

Section: Heuristic Algorithm Designmentioning

confidence: 99%

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker

Sciancalepore¹,

Costa‐Pérez²,

Banchs

2019

IEEE/ACM Trans. Networking

106

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Network slicing is considered one of the main pillars of the upcoming 5G networks. Indeed, the ability to slice a mobile network and tailor each slice to the needs of the corresponding tenant is envisioned as a key enabler for the design of future networks. However, this novel paradigm opens up to new challenges such as isolation between network slices, the allocation of resources across them and the admission of resource requests by network slice tenants. In this work, we address this problem by designing the following building blocks for supporting network slicing: i) traffic and user mobility analysis, ii) a learning and forecasting scheme per slice, iii) optimal admission control decisions based on spatial and traffic information, and iv) a reinforcement process to drive the system towards optimal states. In our framework, namely RL-NSB, infrastructure providers perform admission control considering the Service Level Agreements (SLA) of the different tenants as well as their traffic usage and user distribution, and enhance the overall process by means of learning and reinforcement techniques that consider heterogeneous mobility and traffic models among diverse slices. Our results show that, by relying on appropriately tuned forecasting schemes, our approach provides very substantial potential gains in terms of system utilization while meeting the tenants' SLAs.

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Section: Heuristic Algorithm Designmentioning

confidence: 99%

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker

Sciancalepore¹,

Costa‐Pérez²,

Banchs

2019

IEEE/ACM Trans. Networking

106

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“…On the other hand, when the BoND-tree is created as a temporary indexing structure, the query I/O is usually not the only (or the most important) consideration: sometimes people want to build index trees quickly and discard them after performing a limited number of queries. In such cases, the BoND-tree could be generated using algorithms introduced in [17] and [24], which provide approximate solutions with guaranteed closeness to the optimal solution with much a less time complexity and system resource requirements.…”

Section: End If 23 End Ifmentioning

confidence: 99%

The BoND-Tree: An Efficient Indexing Method for Box Queries in Nonordered Discrete Data Spaces

Chen

Watve

Pramanik

et al. 2013

IEEE Trans. Knowl. Data Eng.

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Abstract-Box queries (or window queries) are a type of query which specifies a set of allowed values in each dimension. Indexing feature vectors in the multi-dimensional Non-ordered Discrete Data Spaces (NDDS) for efficient box queries is becoming increasingly important in many application domains such as genome sequence databases. Most of the existing work in this field targets the similarity queries (range queries and k-NN queries). Box queries, however, are fundamentally different from similarity queries. Hence the same indexing schemes designed for similarity queries may not be efficient for box queries. In this paper, we present a new indexing structure specifically designed for box queries in the NDDS. Unique characteristics of the NDDS are exploited to develop new node splitting heuristics. For the BoND-tree, we also provide theoretical analysis to show the optimality of the proposed heuristics. Extensive experiments with synthetic data demonstrate that the proposed scheme is significantly more efficient than the existing ones when applied to support box queries in NDDSs. We also show effectiveness of the proposed scheme in a real world application of primer design for genome sequence databases.

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“…The significant of the 0/1 knapsack problem has gained from its enormous application, such as the resource allocation problems which occur in the financial field, the industrial field, the architecture and other fields. To solve these critical problems, various optimization algorithms were proposed to solve the 0/1 knapsack problem, such as the simulate annealing [8,9], the genetic algorithms [10,11], the swarm optimization [12,13] and the ant system (AS) optimization [14]. The differences between these optimization algorithms with respect to the 0/1 knapsack problem in specific and optimization problems in general, are: the ability to find the optimal solution, the quality of the generated solution, the non-trapping in local optima and the speed of convergence.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony System Algorithm with Dynamic Pheromone Updating for 0/1 Knapsack Problem

Alzaqebah¹,

Abu-Shareha²

2019

IJISA

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The 0/1 Knapsack (KP) is a combinatorial optimization problem that can be solved using various optimization algorithms. Ant Colony System (ACS) is one of these algorithms that is operated iteratively and converged emphatically to a matured solution. The convergence of the ACS depends mainly on the heuristic patterns that are used to update the pheromone trails throughout the optimization cycles. Although, ACS has significant advantages, it suffers from a slow convergence, as the pheromones, which are used to initiate the searching process are initialized randomly at the beginning. In this paper, a new heuristic pattern is proposed to speed up the convergence of ACS with 0/1 KP. The proposed heuristic enforces an order-critical item selection. As such, the proposed heuristic depends on considering the profit added by each item, as similar to the existing heuristics, besides the order of item selection. Accordingly, the proposed heuristic allows the items that are added at the end to get more value in order to be considered in the beginning of the next round. As such, with each cycle, the selected items are varied substantially and the pheromones are vastly updated in order to avoid long trapping with the initial values that are initialized randomly. The experiments showed that the proposed heuristic is converged more rapidly compared to the existing heuristics by reducing up to 30% of the cycles required to reach the optimal solution using difficult 0/1 KP datasets. Accordingly, the times required for convergence have been reduced significantly in the proposed work compared to the time required by the existing algorithms.

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Improved Simulated Annealing Algorithm Solving for 0/1 Knapsack Problem

Cited by 25 publications

References 2 publications

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker

RL-NSB: Reinforcement Learning-Based 5G Network Slice Broker

The BoND-Tree: An Efficient Indexing Method for Box Queries in Nonordered Discrete Data Spaces

Ant Colony System Algorithm with Dynamic Pheromone Updating for 0/1 Knapsack Problem

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