Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

Feng, Hao; Chen, Ching-Yi; Ye, Fun

doi:10.1016/j.eswa.2005.11.012

Cited by 81 publications

(34 citation statements)

References 12 publications

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“…PSO can optimize multidimensional nonlinear functions, and has been applied to several real-world problems, e.g. [9,21,19,7]. Ant Colony Optimization (ACO) [4] can be thought of as a swarm whose individual agents are ants.…”

Section: Introductionmentioning

confidence: 99%

A Swarm Random Walk Algorithm for Global Continuous Optimization

Altwaijry

Menaï

2014

Advances in Intelligent Systems and Computing

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Abstract. Many real-world problems are modeled as global continuous optimization problems with a nonlinear objective function. Stochastic methods are used to solve these problems approximately, when solving them exactly is impractical. In this class of methods, swarm intelligence (SI) presents metaheuristics that exploit a population of interacting agents able to self-organize, such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC). This paper presents a new SI-based method for solving continuous optimization problems. The new algorithm, called Swarm Random Walk (SwarmRW), is based on a random walk of a swarm of potential solutions. SwarmRW is validated on test functions and compared to PSO and ABC. Results show improved performance on most of the test functions.

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Section: Introductionmentioning

confidence: 99%

A Swarm Random Walk Algorithm for Global Continuous Optimization

Altwaijry

Menaï

2014

Advances in Intelligent Systems and Computing

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“…The PSO proposed by Kennedy and Eberhart (1995) has exhibited effectiveness and robustness in many applications, such as evolving artificial neural networks (Eberhart & Shi, 1998), reactive power and voltage control (Yoshida & Kawata, 1999), state estimation for electric power distribution systems (Shigenori et al, 2003), and image compression (Feng et al, 2007). PSO has drawn on a sociocognition model to gain recognition as a useful global optimizer.…”

Section: Exploitation Of Guidance Informationmentioning

confidence: 99%

A Complementary Cyber Swarm Algorithm

Yin

Glover

Laguna

et al. 2013

Recent Algorithms and Applications in Swarm Intelligence Research

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A recent study (Yin et al., 2010) Standard PSO 2007 method (Clerc, 2008. showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to thewere previously found as a basis for focusing the search in regions anticipated to harbor additional solutions of high quality. Diversification promotes the exploration of regions appreciably different from those previously examined in order to produce new solutions with characteristics that depart from those already seen. Intensification and diversification work together to identify new promising regions when the slave heuristics stagnate in the executed search courses. Many intelligent algorithms fall in the territory of metaheuristics. DOI: 10.4018/jsir.2011040102 International Journal of Swarm Intelligence Research, 2(2), 22-41, April-June 2011 23 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.Some exemplary algorithms (Luke, 2009) are genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), scatter search (SS), greedy randomized adaptive search procedure (GRASP), variable neighborhood search (VNS), to name a few. A recent survey and descriptive analysis of metaheuristic algorithms can be found in Sorensen and Glover (2010).Slave heuristics embedded in metaheuristic methods often adopt solution combination or neighborhood exploration processes to generate new solutions based on the current state of search. Solution combination approaches produce new solutions by exchanging information between candidate solutions (for example, crossover operation executed in GA) or by using candidate solutions as guiding points for producing new solutions (for example, by reference to the best experiences in PSO or the path relinking (PR) process used in SS). Alternatively, neighborhood exploration employs incremental changes, called ...

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“…The particles move through the search space with velocities which are dynamically adjusted according to their historical behavior [12], [13], [14]. PSO has found diverse and useful application in a number of disciplines [15], [16], including fractal image compression [17], [18] and lossless data compression [19]. PSO has also been shown to be effective in mutiobjective optimization [20], [21], [22], [23], [24].…”

Section: Particle Swarmmentioning

confidence: 99%

Image compression and recovery through compressive sampling and particle swarm

Sturgill

Ruitenbeek

Marks

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-We present an application of particle swarm techniques to the problem of sparse signal recovery. Although a direct application of particle swarm is straightforward, specifics of the signal recovery problem can be incorporated into particle behavior in a way that substantially improves the quality of the recovered signal. With encouraging results for synthetic signals, we apply this technique to the problem of image compression, where typical image blocks can be expected to exhibit many very small elements under a transformation like the DCT. In this application, we observe that better results are obtained by first forcing image blocks to be sparse rather than compressively sampling blocks that are approximately sparse.

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Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression

Cited by 81 publications

References 12 publications

A Swarm Random Walk Algorithm for Global Continuous Optimization

A Swarm Random Walk Algorithm for Global Continuous Optimization

A Complementary Cyber Swarm Algorithm

Image compression and recovery through compressive sampling and particle swarm

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