Cat Swarm Optimization algorithm for optimal linear phase FIR filter design

Saha, Suman Kumar; Ghoshal, Sakti Prasad; Kar, Rajib; Mandal, Durbadal

doi:10.1016/j.isatra.2013.07.009

Cited by 170 publications

(58 citation statements)

References 33 publications

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“…The proposed methodology that has been tested on a 10 unit generating system to solve TUC and DRUC problem is shown in Tables 6-8. The parameters assumed here are as follows; population size = 50, max iterations cycles = 100, SMP = 5, CDC = 0.6, SRD = 2, MR = 0.1, inertia weight w = 0.4 and acceleration constant C = 1.5 [41]. Table 6.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

2017

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Over the recent years there has been an immense growth in load consumption due to which, Load Management (LM) has become more significant. Energy providers around the world apply different load management concepts and techniques to improve the load profile. In order to reduce the stress over the load management, Demand Response Unit Commitment (DRUC), a new concept, has been implemented in this paper. The main feature of this concept is that both the energy providers and consumers must participate in order to get mutual benefits hence maximizing each of their profits. In this paper we discuss the time-based Demand Response Program since there is no penalty observed in this program. When the Demand Response was combined with Unit Commitment and compiled it was observed that a satisfactory solution resulted, which is proved to be mutually beneficial for both Generating Companies (GENCOs) and their customers. Here, we have used a Cat Swarm Optimization (CSO) technique to find the solution for the DRUC problem. The results are obtained using CSO technique for UC problem with and without DR program. This is compared with the results obtained using other conventional methods. The test system considered for the study is IEEE39 bus system.

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Section: Simulation Resultsmentioning

confidence: 99%

“…CSO optimization overcomes the limitations of PSO and DE that they are influenced by parameters and stagnation problem [41]. CSO is a meta-heuristic evolutionary optimization technique that intimates the natural behavior of felines.…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

2017

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“…RPSODE (Random PSO in hybrid with DE) 30 is used for the design of linear phase low pass and high pass FIR filters, which produce better magnitude response and convergence speed than PSO, DE and PSODE. Cat Swarm Optimization (CSO) 31 is used for optimal linear phase FIR filter design. The algorithm is generated by observing the behaviour of cats.…”

Section: Evolutionary Identification Methodsmentioning

confidence: 99%

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

Chen¹,

Liu²,

Yang³

et al. 2017

IJCIS

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Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response, and take less additional consideration of structure characteristics which can greatly affect the performance of the digital filter. This paper proposes a structure-evolution based optimization algorithm (SEOA) which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic-generation algorithm (SAGA) which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm (GA) for the search of the optimal solution in structure space. They are evaluated according to the mean squared error (MSE) between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.

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“…The constraints impacted by parameters and stagnation issue of Particle Swarm Optimization (PSO) and Differential Evolution (DE) are met by CSO. CSO is a meta-heuristic transformative optimization approach that imitates the characteristic conduct of felines [26]. The main significance of the feline group is that it has a solid interest towards articles that move, and that of the cat group is that it has prevalent chasing aptitudes.…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

CSO Based Solution for Load Kickback Effect in Deregulated Power Systems

2017

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With increase in power demand, load demand values have also risen to a greater extent. Sometimes, these demands are met with the great difficulties. All these difficulties drive us to seek other alternative ways. One such a way demand response (DR) is considered in this paper, it is a new concept that is introduced in the system in order to reduce peak hour stresses. When implementing the demand response, the main setbacks that arise is the load kickback effect, which the sudden rise in demand during non-peak hours that is caused by the overuse of power by consumers, after their constant reduction of power during peak hours. This paper discusses the various kickback load types, and an effective approach to avoid and tackle kickback effect, by an effective method Cat Swarm Optimization (CSO), which is based on studying the movement of cats. The optimization has been implemented on an IEEE 30 bus and 75 bus Indian utility system, and the results are discussed.

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Cat Swarm Optimization algorithm for optimal linear phase FIR filter design

Cited by 170 publications

References 33 publications

Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit

Structure Evolution Based Optimization Algorithm for Low Pass IIR Digital Filter Design

CSO Based Solution for Load Kickback Effect in Deregulated Power Systems

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