Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack Algorithm

Han, Zhonghua; Tian, Xutian; Ma, Xiaofu; Shao, Lin

doi:10.1088/1757-899x/382/3/032007

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Zhang et al [ 15 ] proposed a hybrid prediction model of WPA based on fuzzy clustering and least squares vector machine, and applied this model to electric bus, which obtained high prediction accuracy and stability. Han et al [ 16 ] used the WPA for scheduling in the RHFS problem, and the experimental results show that the Wolf pack algorithm can effectively solve the existing scheduling problems in the RHFS problem. Gao et al [ 17 ] proposed a WPA based on quantum coding to solve the 0–1 knapsack problem, it is showed that the algorithm had better global search ability by simulation.…”

Section: Related Workmentioning

confidence: 99%

An improved Wolf pack algorithm for optimization problems: Design and evaluation

et al. 2021

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Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.

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Section: Related Workmentioning

confidence: 99%

An improved Wolf pack algorithm for optimization problems: Design and evaluation

et al. 2021

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“…To pact with function optimization issue, the Wolf Pack approach imitates the hunting wolves' behavior, and categorizes the wolves into three stages: the head, search, and fierce Wolf [8].…”

Section: Wp Algorithmmentioning

confidence: 99%

“…In this work, the hybrid algorithm refers to the WPA [8] and PSO [9]. In addition, the maximum likelihood algorithm is exploited to obtain a solution of l and m , which are GO model parameters [7].…”

Section: Algorithm Modelmentioning

confidence: 99%

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

Wang¹

2020

JNACS

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In the WSNs environment, the methodological development ensued in the process of gathering and forwarding the enormous data among the nodes that is the most important challenges in WSNs as it is related with high energy loss and delay. This ensued in establishment of a routing protocol for the optimally chosen of multipath to development of a routing in WSNs. Hence, this work presents an energy-effectual routing in WSNs utilizing the hybrid Wolf Pack approach with Particle Swarm Optimization (WP-PSO) approach that selects the optimal hops in succeeding the routing. Initially, the CHs are chosen by exploiting the LEACH protocol which reduces the traffic in the network. In the multihop routing, the CHs are engaged, and the selection of the optimal paths is based on the developed hybrid optimization that chooses optimal hops on basis of the energy constraints, namely energy, inter-intracluster distance, link lifetime, delay, and distance. The experimentation outcomes show that developed routing protocol obtained maximum energy, minimum delay, the maximum number of the alive nodes and average throughput.

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“…Menassel et al provided more detailed study about the Wolf Pack Algorithm for the fractal image compression in the literature [10]. In order to solve the scheduling problems of Re-entrant Hybrid Flowshop (RHFS), Han et al investigated the mathematical programming model of RHFS, and proposed the Wolf Pack Algorithm (WPA) as a global optimization method [11]. In view of the local extreme problem of the gradient descent algorithm which makes the working face of mine gas emission prediction uncertainly, Xu et al combined Wolf Pack Algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model [12].…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (Wolf Pack Algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

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Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack Algorithm

Cited by 6 publications

References 9 publications

An improved Wolf pack algorithm for optimization problems: Design and evaluation

An improved Wolf pack algorithm for optimization problems: Design and evaluation

Hybrid Wolf Pack and Particle Swarm Optimization Algorithm for Multihop Routing Protocol in WSN

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

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