Improved salp swarm algorithm based on weight factor and adaptive mutation

Wu, Jun; Nan, Ruijie; Chen, Lei

doi:10.1080/0952813x.2019.1572659

Cited by 63 publications

(29 citation statements)

References 29 publications

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“…e target state given in the initial frame in target tracking is shown in equation (5), where x i , y i is the upper left corner of the target Ground Truth, and w i , h i denote the width and height of the tracked target, respectively. e real value given in the initial frame is used as the initialized positive sample of the tracked target, while the negative sample is obtained by random sampling around the real position.…”

Section: Adaptive Scale Target Tracking Algorithmmentioning

confidence: 99%

“…Because visual target tracking technology has more outstanding advantages and wide applications than detection and recognition in the field of computer vision, it has attracted a wave of research from industry and scholars in recent years. However, many challenges in its research have plagued many researchers, such as illumination changes, scale changes, fast deformation, motion blur, background mottling, and target occlusion, which can affect tracker performance [5]. For target tracking systems, tracker performance is not only affected by the degree of model merit but is also related to the variation of the target itself and the environment.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Yang

2021

Complexity

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In this paper, to better solve the problem of low tracking accuracy caused by the sudden change of target scale, we design and propose an adaptive scale mutation tracking algorithm using a deep learning network to detect the target first and then track it using the kernel correlation filtering method and verify the effectiveness of the model through experiments. The improvement point of this paper is to change the traditional kernel correlation filtering algorithm to detect and track at the same time and to combine deep learning with traditional kernel correlation filtering tracking to apply in the process of target tracking; the addition of deep learning network not only can learn more accurate feature representation but also can more effectively cope with the low resolution of video sequences, so that the algorithm in the case of scale mutation achieves more accurate target tracking in the case of scale mutation. To verify the effectiveness of this method in the case of scale mutation, four evaluation criteria, namely, average accuracy, cross-ratio accuracy, temporal robustness, and spatial robustness, are combined to demonstrate the effectiveness of the algorithm in the case of scale mutation. The experimental results verify that the joint detection strategy plays a good role in correcting the tracking drift caused by the subsequent abrupt change of the target scale and the effectiveness of the adaptive template update strategy. By adaptively changing the number of interval frames of neural network redetection to improve the tracking performance, the tracking speed is improved after the fusion of correlation filtering and neural network, and the combination of both is promoted for better application in target tracking tasks.

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Section: Adaptive Scale Target Tracking Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

Yang

2021

Complexity

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“…All function dimensions were 30, and the scope selected in the range of [-1,1]. Compared algorithms in this paper selected butterfly optimization algorithm (BOA) [64], Grey Wolf Optimizer [65], the lé vy-flight salp swarm algorithm (LSSA) [66], sine cosine algorithm (SCA) [67], salp swarm algorithm (SSA) [68], improved SSA based on weight factor and adaptive mutation (WASSA) [69], whale optimization algorithm (WOA) [70]. BOA was proposed by Arora and Singh, modular modality c selected 0.01 and power exponent a selected from 0.1 to 0.3.…”

Section: Cec Test Suits Experiments Aexperimental Parameters and Environmentmentioning

confidence: 99%

An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

Shao

Fan

2021

IEEE Access

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Aiming at the shortcoming that the basic beetle antennae search algorithm fails to consider the differences between individuals and dynamic information in the searching process, this paper proposed a new search algorithm based on the elite selection mechanism and the neighbor mobility strategy. The elitist selection mechanism is used to weaken beetles having bad performance and generate new beetles to ensure diversity and adaptability in the whole population. The neighbor mobility strategy will guide the algorithm to open up a wider searching area to ensure that individuals having good positions owns a chance to infect individuals with poor performances. To verify the searching ability and the optimization speed of the proposed algorithm in this paper, different testing functions were selected for numerical testing experiments, and the iteration figures, box plots, and searching path figures were given to conduct a comprehensive statistical test and analysis. The experimental results show that the proposed algorithm in this paper is superior to the original algorithm and other comparative algorithms in solving accuracy, convergence speed, and stability.INDEX TERMS Beetle antennae search algorithm, numerical problems, global optimization.

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“…SSA has only one main controlling parameter, so it is simple and easy to implement. However, like other swarm-based algorithms, SSA has the insufficiencies of low convergence precision and slow convergence speed when dealing with highdimensional complex optimization problems [23]. In the classical SSA optimization process, global exploration and local exploitation are a pair of contradictions.…”

Section: Performance Optimizationmentioning

confidence: 99%

Performance Optimization of Cloud Data Centers with a Dynamic Energy‐Efficient Resource Management Scheme

2021

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As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines (PMs) into two pools: a hot (running) pool and a warm (turned on, but in dynamic sleep) pool. Each PM is configured with a resource search engine (RSE) that finds an available virtual machine (VM) for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm (SSA) is presented to minimize the system cost and optimize the sleep parameter.

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Improved salp swarm algorithm based on weight factor and adaptive mutation

Cited by 63 publications

References 29 publications

[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

[Retracted] Target Tracking Algorithm Based on Adaptive Scale Detection Learning

An Improved Beetle Antennae Search Algorithm Based on the Elite Selection Mechanism and the Neighbor Mobility Strategy for Global Optimization Problems

Performance Optimization of Cloud Data Centers with a Dynamic Energy‐Efficient Resource Management Scheme

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