A memory-based simulated annealing algorithm and a new auxiliary function for the fixed-outline floorplanning with soft blocks

Zou, Dexuan; Wang, Gai-Ge; Sangaiah, Arun Kumar; Kong, Xiangyong

doi:10.1007/s12652-017-0661-7

Cited by 13 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers have used a memory buffer and/or a restart mechanism to optimize the SA algorithm. For example, Zou et al (2017) utilized a triplememory buffer to solve the problem of fixed outline floor-planning with soft blocks which periodically stores the best solution in a memory buffer and extracts the oldest solution, and when the number of sequential failures reaches the maximum allowed limit, the algorithm refers to one of the solutions stored in the memory and the temperature is reset adaptively. In a paper by Vincent et al (2017), to solve the vehicle routing problem (VRP), if the best solution was not optimized after every 10 rounds of temperature reduction, the SA algorithm was reset to the initial temperature and a primary new solution was generated.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel memory-based simulated annealing algorithm to solve multi-line facility layout problem

Zolfi¹,

Jouzdani²,

Shirouyehzad³

2023

10.5267/j.dsl

View full text Add to dashboard Cite

In this paper, a memory-based simulated annealing algorithm called the Dual Memory Simulated Annealing Algorithm (DMSA) is presented to solve multi-line facility layout problems. The objective is to minimize the total material handling cost. Two memory buffers and a restart mechanism are considered. Two benchmark problems were selected from the literature review papers and solved using the standard simulated annealing (SA) algorithm and the DMSA. The obtained results show that solutions provided by the DMSA algorithm are cost-effective compared to the standard SA algorithm and the other algorithms used for solving these test cases. Moreover, to further evaluate the performance of the DMSA algorithm in large scale problems, eleven test cases were selected from the benchmark library of the quadratic assignment problem (QAP). According to the results, the performance of the algorithm in finding solutions to complex problems is exemplary.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A novel memory-based simulated annealing algorithm to solve multi-line facility layout problem

Zolfi¹,

Jouzdani²,

Shirouyehzad³

2023

10.5267/j.dsl

View full text Add to dashboard Cite

show abstract

“…For more general non-slicing floorplan representations, several effective forms have been developed, including sequence pairs (SP) [9], the bounded slicing grid (BSG) [10], O-trees [11], transitive closure graphs with packed sequences (TCG-S) [12], and B*-trees [13]. Among these, the representation of block placement with sequence pairs, which uses positive and negative sequences to represent the geometric relationships between any two modules, has been extended in subsequent work to handle obstacles [14], soft modules, rectilinear blocks, and analog floorplans [15][16][17][18]. The decoding time complexity of the sequence pair representation is O(N 2 ).…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs

Yu,

Du,

Yang

2024

Applied Sciences

View full text Add to dashboard Cite

In integrated circuit (IC) design, floorplanning is an important stage in obtaining the floorplan of the circuit to be designed. Floorplanning determines the performance, size, yield, and reliability of very large-scale integration circuit (VLSI) ICs. The results obtained in this step are necessary for the subsequent continuous processes of chip design. From a computational perspective, VLSI floorplanning is an NP-hard problem, making it difficult to be efficiently solved by classical optimization techniques. In this paper, we propose a deep reinforcement learning floorplanning algorithm based on sequence pairs (SP) to address the placement problem. Reinforcement learning utilizes an agent to explore the search space in sequence pairs to find the optimal solution. Experimental results on the international standard test circuit benchmarks, MCNC and GSRC, demonstrate that the proposed deep reinforcement learning floorplanning algorithm based on sequence pairs can produce a superior solution.

show abstract

“…Metaheuristic methods are used to solve complicated issues such as scheduling (L. Wang and Zheng 2018), shape design (Rizk-Allah et al 2017), economic load dispatch , large-scale data optimization (Yi et al 2018(Yi et al ,2020, infinite impulse response (IIR) systems identification ( Zou et al 2018), malware code detection (Cui et al 2018), error detection ( Li et al 2013;Yi et al 2018), forecasting promotion places (Nan et al 2017), unit commitment (Srikanth et al2018), classification ( Zou et al ,2017, path planning (Wang et al 2012, vehicle navigation ), knapsack problems , and cyber-physical systems (Cui et al 2017), neural network (Pandey et al 2020), trajectory tracking control of unmanned aerial vehicle (Selma et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

et al. 2022

View full text Add to dashboard Cite

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled "search in forest (SIF) optimizer" to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIF optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intra-group search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms.

show abstract

A memory-based simulated annealing algorithm and a new auxiliary function for the fixed-outline floorplanning with soft blocks

Cited by 13 publications

References 46 publications

A novel memory-based simulated annealing algorithm to solve multi-line facility layout problem

A novel memory-based simulated annealing algorithm to solve multi-line facility layout problem

A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs

Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

Contact Info

Product

Resources

About