Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Pant, Millie; Thangaraj, Radha; Abraham, Ajith

doi:10.1007/978-3-642-01085-9_5

Cited by 53 publications

(41 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean best fitness values (stand for the optimization ability of the algorithm) and standard deviations (stand for the algorithm's stability) are reported in Table 6. The data of five comparing algorithms are from the work of Pant et al (2009). Table 5 shows the common set of all the algorithms in Table 6.…”

Section: Comparison With Five Other Initialization Methodsmentioning

confidence: 99%

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Liu

Zhu

Chen

et al. 2015

Struct Multidisc Optim

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is a relatively new global optimization algorithm. Benefitting from its simple concept, fast convergence speed and strong ability of optimization, it has gained much attention in recent years. However, PSO suffers from premature convergence problem because of the quick loss of diversity in solution search. In order to improve the optimization capability of PSO, design of experiment method, which spreads the initial particles across a design domain, and data mining technique, which is used to identify the promising optimization regions, are studied in this research to initialize the particle swarm. From the test results, the modified PSO algorithm initialized by OLHD (Optimal Latin Hypercube Design) technique successfully enhances the efficiency of the basic version but has no obvious advantage compared with other modified PSO algorithms. An extension algorithm, namely OLCPSO (Optimal Latin hypercube design and Classification and Regression tree techniques for improving basic PSO), is developed by consciously distributing more particles into potential optimal regions. The proposed method is tested and validated by benchmark functions in contrast with the basic PSO algorithm and five PSO variants. It is found from the test studies that the OLCPSO algorithm successfully enhances the efficiency of the basic PSO and possesses competitive optimization ability and algorithm stability in contrast to the existing initialization PSO methods.

show abstract

Section: Comparison With Five Other Initialization Methodsmentioning

confidence: 99%

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Liu

Zhu

Chen

et al. 2015

Struct Multidisc Optim

View full text Add to dashboard Cite

show abstract

“…One of the drawbacks of most population-based search techniques is that they work on the principle of contracting the search domain toward the global optima (Pant, Thangaraj, & Abraham, 2009). This phenomenon usually leads to the problem of insufficient diversity, in which offspring, outperforming their parents, can no longer be produced after a certain number of evolutionary iterations; thus, all particles stay trapped in a region, which may not even contain local optima.…”

Section: Development Of the Mathematical Optimization Modelmentioning

confidence: 99%

Optimal design of multi-storage network for combined sewer overflow management using a diversity-guided, cyclic-networking particle swarm optimizer – A case study in the Gunja subcatchment area, Korea

Baek

Ryu

et al. 2015

Expert Systems with Applications

View full text Add to dashboard Cite

“…rate of change) and the difference between its current position, respectively the best position found by its neighbors, and the best position it has found so far. As the model is iterated, the swarm focuses more and more on an area of the search space containing high-quality solutions [32]. We have to note that PSO is mainly used for continuous optimization while ACO is mainly used for combinatorial optimization.…”

Section: Swarm Intelligencementioning

confidence: 99%

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Soliman

Hassanien

Onsi

2014

Bio-Inspiring Cyber Security and Cloud Services: Trends and Innovations

View full text Add to dashboard Cite

Bio-inspiring (BI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. BI is considered as one of the most important increasing fields, which attract a large number of researchers and scientists working in areas such as neuro-computing, global optimization, swarms and evolutionary computing. On the other hand, digital radiological modalities in modern hospitals have led to the producing a variety of a vast amount of digital medical files. Therefore, for the medical imaging, the authenticity needs to ensure the image belongs to the correct patient, the integrity check to ensure the image has not been modified, and safe transfer are very big challenges. The integrity of the images must be protected by using watermarking, which is called integrity watermark. At the same time the copyright and intellectual property of the medical images should be also protected, which is called copyright watermark. This chapter presents a brief overview of well known Bio-inspiring techniques including neural networks, genetic algorithm, swarms and evolutionary algorithms and show how BI techniques could be successfully employed to solve watermarking problem. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.

show abstract

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Cited by 53 publications

References 28 publications

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Optimal design of multi-storage network for combined sewer overflow management using a diversity-guided, cyclic-networking particle swarm optimizer – A case study in the Gunja subcatchment area, Korea

Bio-inspiring Techniques in Watermarking Medical Images: A Review

Contact Info

Product

Resources

About