An Improved Partical Swarm Optimization Algrithm

Zhang, Baojian; Chen, Fugui; Bai, Linfeng

doi:10.4304/jcp.6.11.2460-2467

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…PSO-PCNN optimizes PCNN parameters depending on PSO while the proposed method optimizes PCNN parameters through combining with immune algorithm. LI Linyi et al [17] indicate that immune algorithm compared with PSO has the higher global optimization ability and the ability of jumping out of local optimal solution. Therefore, the proposed method combined with immune algorithm can obtain the more reasonable parameters than PSO-PCNN.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

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Image Segmentation with PCNN Model and Immune Algorithm

Li¹,

Zou²,

Ding³

et al. 2013

JCP

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In the domain of image processing, PCNN (Pulse Coupled Neural Network) need to adjust parameters time after time to obtain the better performance. To this end, we propose a novel PCNN parameters automatic decision algorithm based on immune algorithm. The proposed method transforms PCNN parameters setting problem into parameters optimization based on immune algorithm. It takes image entropy as the evaluation basis of the best fitness of immune algorithm so that PCNN parameters can be adjusted adaptively. Meanwhile, in order to break the condition that population information fall into local optimum, the proposed method introduces gradient information to affect the evolution of antibody to keep the population activity. Experiment results show that the proposed method realizes the adaptive adjustment of PCNN parameters and yields the better segmentation performance than many existing methods.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…value of equation[17] by OTSU, Maximum entropy, Cross entropy, Watershed, Region consistency, PCNN, PSO-PCNN and the proposed method for test image.…”

mentioning

confidence: 99%

Image Segmentation with PCNN Model and Immune Algorithm

Li¹,

Zou²,

Ding³

et al. 2013

JCP

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“…Counter proposal is expressed as b=(b 1 ,b 2 ,…,b Hypothesis space. We use PSO algorithm [17][18][19] to solve the optimization problem.…”

Section: Negotiation Counter-proposal Generationmentioning

confidence: 99%

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Zheng

Peng

2014

JSW

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Negotiation has been extensively discussed in electronic commerce for decades. Recent growing interest in importing machine learning algorithm in electronic commerce has given increased importance to automated negotiation. A Tri-Training based algorithm was proposed to learn opponent's negotiation preference. The process of negotiation was viewed as a proposal's sequence which can be mapped into bidding trajectory feature space to form sample set. Due to fierce competition, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, Tri-Training, as a semisupervised method, was imported into negotiation framework to increase the number of samples and improve perdition accuracy of opponent's negotiation preference learning. Based on negotiation preference of both side, an optimization algorithm is conducted to compute win-win counter proposal. The experimental results show that the proposed method can decrease the number of negotiation steps and increase the overall utility of negotiation.

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“…Owing to its outstanding characters, PSO has been an effective tool for solving global optimization problems during the past two decades, such as non-linear function optimization, signal processing [2], image reconstruction [3], engineering optimization [4], etc. Although PSO has obtained some promising results in many research fields, many experiments also indicate that the traditional PSO algorithm easily falls into local optima while solving complex multimodal problems.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Algorithm with Reverse-Learning and Local-Learning Behavior

Xia¹,

Liu²,

Li³

2014

JSW

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In order to resolve conflict between convergence speed and population diversity of particle swarm optimization (PSO) algorithm, an improved PSO, called reverse-learning and local-learning PSO (RLPSO) algorithm, is presented in which a reverse-learning behavior implemented by some particles while local-learning behavior adopted by elite particles in each generation. During the reverse-learning process, some inferior particles of initial population and each particle's historical worst position are reserved to attract a particle to leap out of local optimums. Furthermore, the Hamming distance between the inferior particles is set to no less than a default rejection distance the aim of which is to maintain the diversity of population and improve RLPSO's exploration ability. In each generation, the difference between the best particle and the second-best particle is used to guide the best one to carry out local search which is crucial for improving RLPSO's exploitation ability. The results of experiments show that RLPSO has a good global searching ability and convergence speed especially in high dimension function.

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An Improved Partical Swarm Optimization Algrithm

Cited by 7 publications

References 20 publications

Image Segmentation with PCNN Model and Immune Algorithm

Image Segmentation with PCNN Model and Immune Algorithm

Tri-Training based Bilateral Multi-Issue Negotiation Framework

Particle Swarm Optimization Algorithm with Reverse-Learning and Local-Learning Behavior

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