A chaos search immune algorithm with its application to neuro-fuzzy controller design

Zuo, Xingquan; Yang, Fan

doi:10.1016/j.chaos.2005.08.126

Cited by 79 publications

(31 citation statements)

References 18 publications

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“…For problems with multiple decision variables, chaos search is more capable of hill-climbing and escaping from the local optima than the random search [35]. Hence a chaos optimization algorithm is proposed to solve the problem The detailed procedure of the algorithm is given as follows:…”

Section: Solution Methodology For Independent System Operator's Initimentioning

confidence: 99%

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Designing an Incentive Contract Menu for Sustaining the Electricity Market

Jin

Zhong

2015

Energies

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This paper designs an incentive contract menu to achieve long-term stability for electricity prices in a day-ahead electricity market. A bi-level Stackelberg game model is proposed to search for the optimal incentive mechanism under a one-leader and multi-followers gaming framework. A multi-agent simulation platform was developed to investigate the effectiveness of the incentive mechanism using an independent system operator (ISO) and multiple power generating companies (GenCos). Further, a Q-learning approach was implemented to analyze and assess the response of GenCos to the incentive menu. Numerical examples are provided to demonstrate the effectiveness of the incentive contract.

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Section: Solution Methodology For Independent System Operator's Initimentioning

confidence: 99%

“…PC : 35) where C(a 0 ) is the total power purchasing cost when the combination of the target GenCos is a 0 , and δ is a balance parameter. EP(a 0 ) is the expected electricity price when the combination of the target GenCos is a 0 , and EP* is the best expected price.…”

Section: Optimization Problem Of Independent System Operatormentioning

confidence: 99%

Designing an Incentive Contract Menu for Sustaining the Electricity Market

Jin

Zhong

2015

Energies

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“…Due to the ergodicity property of chaotic sequences, it will lead to very different future solution-finding behaviors, thus, chaotic sequences can be used to enrich the search behavior and to avoid being trapped in a local optimum [55]. There are lots of applications in optimization problema using chaotic sequences [56][57][58][59][60]. Coelho and Mariani [61] recently apply a chaotic artificial immune network (chaotic opt-aiNET) to solve the economic dispatch problem (EDP), based on Zaslavsky's map by its spread-spectrum characteristic and large Lyapunov exponent to successfully escape from local optimum and to converge to a stable equilibrium.…”

Section: Chaotic Immune Algorithm (Cia) In Selecting Parameters Of Thmentioning

confidence: 99%

SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting

et al. 2011

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Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that OPEN ACCESSEnergies 2011, 4 961 the proposed model yields more accurate forecasting results than the ARIMA and TF-ε-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.

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“…They have been used to improve the performance of EA's (Alatas et al (2009)) and Caponetto et al (2003)). They have also been used together with some heuristic optimisation algorithms (Davendra et al (2010) and Zuo and Fan (2006)) to express optimisation variables. The choice of chaotic sequences is justified theoretically by their unpredictability, i.e., by their spread-spectrum characteristic, non-periodic, complex temporal behaviour, and ergodic properties (Ozer, 2010).…”

Section: Introductionmentioning

confidence: 99%