An improved fractal image compression using wolf pack algorithm

Menassel, Rafik; Nini, Brahim; Mekhaznia, Tahar

doi:10.1080/0952813x.2017.1409281

Cited by 23 publications

(13 citation statements)

References 36 publications

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“…There are often several scout wolves, with better sense of smell and second only to wolves in fitness. 25 They often explore in multiple directions around the optimal position and randomly change their identities according to changes in fitness, which increases the randomness and accuracy of the optimization algorithm. Ferocious wolves have a poor sense of smell and a large number, which represents the main force of the attack, making the algorithm converge to the global optimum.…”

Section: Division Of Wolvesmentioning

confidence: 99%

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Wang

You

Yue

et al. 2021

International Journal of Distributed Sensor Networks

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Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

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Section: Division Of Wolvesmentioning

confidence: 99%

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Wang

You

Yue

et al. 2021

International Journal of Distributed Sensor Networks

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“…The basic WPA was originally designed for continuous optimization problems. Due to its simple implementation, robustness, and competitive global convergence performance for high-dimension multimodal functions [19][20][21], WPA has attracted increasing attention and its various derivative versions for solving discrete problems have been developed in recent years. In [24], Wu et al proposed a binary WPA (BWPA) based on binary coding of solution to solve the classic 0-1 KPs.…”

Section: Overview Of Binary Wolf Pack Algorithmmentioning

confidence: 99%

“…Wolf pack algorithm (WPA) [19] is a relatively new and promising member of swarm intelligence-based algorithms that model the cooperative hunting behavior of wolf pack. It has been proved an efficient optimizer for solving many nonlinear and complex optimization problems by successful applications in image processing [20], power system control [21], robot path planning [22], and static MKPs [23]. Many derivative versions of WPA also have been designed for solving different problems, such as binary WPA (BWPA) for 0-1 ordinary knapsack problem [24], improved binary WPA (IBWPA) for MKPs [23], and discrete WPA (DWPA) for TSP [25].…”

Section: Introductionmentioning

confidence: 99%

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Xiao

2020

Research

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Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.

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“…Chen et al proposed a modified two-part wolf pack search (MTWPS) algorithm updated by the two-part individual encoding approach as well as the transposition and extension (TE) operation for the multiple travelling salesmen problem (MTSP) [9]. 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].…”

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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An improved fractal image compression using wolf pack algorithm

Cited by 23 publications

References 36 publications

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

A novel topology optimization of coverage-oriented strategy for wireless sensor networks

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

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

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