An efficient GPU-based parallel tabu search algorithm for hardware/software co-design

Hou, Neng; He, Fazhi; Zhou, Yi; Chen, Yilin

doi:10.1007/s11704-019-8184-3

Cited by 58 publications

(23 citation statements)

References 55 publications

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“…where h g is the constant value 8; sf is the constant value 18; and tan(ϕ) represents the gliding angle, which can be calculated by formula (6):…”

Section: Update the Positionsmentioning

confidence: 99%

“…Swarm intelligence imitates the social behavior of animal groups. Because of its simplicity, flexibility, non-derivation mechanism, and avoidance of local optima, SI is widely applied in feature selection [5], hardware/software co-design [6], scheduling [7], agriculture [8], metallurgy [9], and military [10]. The most representative SI algorithms are particle swarm optimization (PSO) [11], ant colony optimization (ACO) [12], and artificial bee colony (ABC) [13].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Squirrel Search Algorithm With Reproductive Behavior

et al. 2020

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The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms. INDEX TERMS Nature-inspired, squirrel search algorithm, exploration and exploitation, Wilcoxon's test, CEC 2014 benchmark functions.

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“…where h g is the constant value 8; sf is the constant value 18; and tan(ϕ) represents the gliding angle, which can be calculated by formula (6):…”

Section: Update the Positionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Squirrel Search Algorithm With Reproductive Behavior

et al. 2020

View full text Add to dashboard Cite

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“…Chen et al (2020) proposed a Biogeography-based Optimization algorithm with Elite Learning. Hou et al (2020) proposed an efficient GPU-based parallel tabu search algorithm. Luo et al (2020) proposed an efficient and robust fusion bat algorithm, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary collaborative optimization based on relaxation method for solving complex problems

Chagraoui

Soula

2020

Concurrent Engineering

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The purpose of the present work is to improve the performance of the standard collaborative optimization (CO) approach based on an existing dynamic relaxation method. This approach may be weakened by starting design points. First, a New Relaxation (NR) method is proposed to solve the difficulties in convergence and low accuracy of CO. The new method is based on the existing dynamic relaxation method and it is achieved by changing the system-level consistency equality constraints into relaxation inequality constraints. Then, a Modified Collaborative Optimization (MCO) approach is proposed to eliminate the impact of the information inconsistency between the system-level and the discipline-level on the feasibility of optimal solutions. In the MCO approach, the impact of the inconsistency is treated by transforming the discipline-level constrained optimization problems into an unconstrained optimization problem using an exact penalty function. Based on the NR method, the performance of the MCO approach carried out by solving two multidisciplinary optimization problems. The obtained results show that the MCO approach has improved the convergence of CO significantly. These results prove that the present MCO succeeds in getting feasible solutions while the CO fails to provide feasible solutions with the used starting design points.

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“…Formally, SI is the collective manners of unsophisticated agents communicating locally with their environment make coherent functional global patterns to emerge. [34][35][36] In a colony, an ant can only do a simple task on its own, while the colony's cooperative activity is intelligent behavior. Although they have no sight, ants can discover the shortest path between their nest and food source by chemical materials named pheromone that they place when moving.…”

Section: Introductionmentioning

confidence: 99%

“…Ant colony optimization (ACO) belongs to the swarm intelligence (SI) that is applied to discover solutions to problems in proper computational time. Formally, SI is the collective manners of unsophisticated agents communicating locally with their environment make coherent functional global patterns to emerge 34‐36 . In a colony, an ant can only do a simple task on its own, while the colony's cooperative activity is intelligent behavior.…”

Section: Introductionmentioning

confidence: 99%

A novel ant colony optimization algorithm for PAPR reduction of OFDM signals

Aghdam

Sharifi

2020

Int J Communication

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SummaryThe high peak‐to‐average power ratio (PAPR) is the main challenge of orthogonal frequency division multiplexing (OFDM) systems. Partial transmit sequence (PTS) is a useful approach to diminish the PAPR. Although the PTS approach significantly decreases the PAPR, it requires to explore all possible sequences of phase weighting factors. Hence, the computational cost exponentially increases with the number of divided subblocks. This paper proposes a novel PTS technique based on ant colony optimization (ACO) to diminish the high PAPR and computational cost of OFDM systems. By the new representation of phase factors as a graph, the improved ACO algorithm is combined with the PTS method to explore the optimal compound of the phase rotation factors. Simulation results represent that the proposed ACO‐based PTS approach significantly reduces the PAPR and improves the computational cost at the same time. A comparative analysis of the other meta‐heuristics shows that the ACO‐PTS approach outperforms the genetic algorithm, particle swarm optimization, and gray wolf optimization in terms of reducing PAPR.

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An efficient GPU-based parallel tabu search algorithm for hardware/software co-design

Cited by 58 publications

References 55 publications

An Improved Squirrel Search Algorithm With Reproductive Behavior

An Improved Squirrel Search Algorithm With Reproductive Behavior

Multidisciplinary collaborative optimization based on relaxation method for solving complex problems

A novel ant colony optimization algorithm for PAPR reduction of OFDM signals

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