Optimal Control of Class of Non-linear Plants Using Artificial Immune Systems: Application of the Clonal Selection Algorithm

Ramaswamy, Srichander; Venayagamoorthy, G.K.; Balakrishnan, S. N.

doi:10.1109/isic.2007.4450893

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…Acan (2004) propose Artificial Immune System with Mutation Multiplicity (AISMM) that uses multiple concurrent mutation operators. Ramaswamy et al (2007) define a goal directed mutation process. Panigrahi et al (2007) utilize binary flip mutation with the probability of mutation varying from 0.010 to 0.035 and use tournament selection.…”

Section: Csa Methods and Applicationsmentioning

confidence: 99%

“…In this study, the problem types that are mainly studied by means of CSA are grouped as follows: function optimization (i.e., multi modal optimization and continuous function optimization), pattern recognition (PR) (i.e., binary character and face detection), design (i.e., continuous design, electromagnetic design, and hardware/software design), scheduling (i.e., job shop scheduling and project scheduling), industrial engineering (IE) related problems (i.e., facility location, layout, assembly planning, and material handling systems), TSP, and The most common type of problem solved by CSA is function optimization (Acan 2004;Cutello et al 2005;De Castro and Von Zuben 2002;Dilettoso and Salerno 2006;Garrett 2004;Du et al 2002;Khilwani et al 2008;Kelsey and Timmis 2003;Liu et al 2009;Ramaswamy et al 2007;Yang et al 2008aYang et al , 2008b. Then, the pattern recognition type problems have a notable number of studies (Garain et al 2006;Li et al 2008;Ma et al 2006;Watkins et al 2003;White and Garrett 2003;and Zhang and Hou 2003) followed by design problems (Akdagli et al 2007;Campelo et al 2004Campelo et al , 2005Dong et al 2007;Garain et al 2006;Moghaddam and Kardan 2009;Wang 2005;Wu et al 2009;and Zuo and Fan 2003).…”

Section: Problem Types Solved With Csamentioning

confidence: 99%

“…Ma et al (2006) compare their CSA and template matching based algorithm results with immune genetic, standard GAs, and evolutionary GAs. Ramaswamy et al (2007) compare their algorithm with the State Dependent Algebraic Riccati Equation (SDARE) method. Cao and Xiao (2007) compare the immune optimization approach with GAs.…”

Section: Csa Comparison With Other Heuristicsmentioning

confidence: 99%

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A review of clonal selection algorithm and its applications

2011

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Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The Clonal Selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research.

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Section: Csa Methods and Applicationsmentioning

confidence: 99%

Section: Problem Types Solved With Csamentioning

confidence: 99%

Section: Csa Comparison With Other Heuristicsmentioning

confidence: 99%

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A review of clonal selection algorithm and its applications

2011

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“…Simulated annealing is very slow; particle swarm such as ant colony is discrete; when the number of parameters increases and when they are epistatic in nature, genetic algorithms cannot find a global optimum [7] whereas AIS is free from the above drawback.…”

Section: Introductionmentioning

confidence: 99%

A novel global optimal path planning and trajectory method based on adaptive dijkstra-immune approach for mobile robot

Asadi

Azimirad

Eslami

et al. 2011

2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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In this paper a new method to find global optimal path is obtained. Utilization of standard graph searching methods leads to eliminate uncertainness of heuristic algorithms. By using graph searching method a suboptimal solution is obtained, it causes to increase speed, precision and performance of heuristic algorithms. Firstly, the environment is defined with using a useful graph theory. Then by adaptive Dijkstra algorithm a suboptimal path is obtained. Finally, Continuous Clonal Selection Algorithm (CCSA) that is combined with negative selection algorithm, improves this suboptimal path and derives global optimal path. The simulation results show that this suggested method in compression with ant colony and elistic genetic algorithms, has more accuracy and precision, with competitive speed. Also, our suggested algorithm can be used for solving more complicated dynamic problems. Moreover, this proposed approach can be used as standard method in optimization problems especially in path planning and trajectory.

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“…Clonal selection algorithm (CSA) is a member of the family of AIS techniques. In the past few years, CSA has been gradually used to solve the optimal control problems [16]- [18]. In this paper, an online CSA-based optimal excitation controller for the electric ship is implemented on the MSK2812 DSP hardware platform to minimize the voltage deviations when high power pulsed loads are directly powered from the dc side; exploring the possibility of reduced energy storage.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of an AIS-Based Optimal Excitation Controller for an Electric Ship

Yan

Venayagamoorthy

Corzine

2008

2008 IEEE Industry Applications Society Annual Meeting

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The operation of high energy loads on Navy's future electric ships, such as directed energy weapons, will cause disturbances in the main bus voltage and impact the operation of the rest of the power system when the pulsed loads are directly powered from the main dc bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using an artificial immune system (AIS) based algorithm. The AIS algorithm, a clonal selection algorithm (CSA), is used to minimize the effects of pulsed loads by improved excitation control and thus, reduce the requirement on energy storage device capacity. The CSA is implemented on the MSK2812 DSP hardware platform. A comparison of CSA and the particle swarm optimization (PSO) algorithm is presented. Hardware measurement results show that the CSA optimized excitation controller provides effective control of a generator's terminal voltage during pulsed loads, restoring and stabilizing it quickly.Index Terms -Clonal selection algorithm (CSA), Electric ship, optimal excitation controller, particle swarm optimization (PSO), pulsed loads.978-1-4244-2279-1/08/$25.00

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Optimal Control of Class of Non-linear Plants Using Artificial Immune Systems: Application of the Clonal Selection Algorithm

Cited by 11 publications

References 9 publications

A review of clonal selection algorithm and its applications

A review of clonal selection algorithm and its applications

A novel global optimal path planning and trajectory method based on adaptive dijkstra-immune approach for mobile robot

Hardware Implementation of an AIS-Based Optimal Excitation Controller for an Electric Ship

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