A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization

Tessema, B.G.; Yen, Gary G.

doi:10.1109/cec.2006.1688315

Cited by 183 publications

(109 citation statements)

References 7 publications

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“…Then the algorithm starts to find feasible solutions in addition to infeasible ones, infeasible solutions with low objective function values being subject to less penalization than infeasible solutions with high objective function values. Details are given in [12]. In later iterations, the population consists of feasible individuals and penalization becomes passive, because v ′ (x) = p (x) = , resulting in e (x) = f (x) for all individuals.…”

Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

“…To discard the selection of a large set of parameters, an adaptive penalization technique is used in this study [12]. This technique does not need parameter tuning and has the ability to set the penalized fitness function at different stages of the optimization.…”

Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

“…The normalized values of the violations and objective functions are represented by v ′ (x) and f ′ (x), respectively [12].…”

Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

“…In this work, for increasing the DSA speed of candidate solutions during optimization, multilayer perceptrons (MLPs) are used to predict stability indices, instead of TDSs and small-signal stability analyses. In addition, self-adaptive penalization [12] is applied to decrease the number of function evaluations required to reach feasible solutions, even if the initial set of candidate solutions is composed of only infeasible solutions.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic security enhancement of power systems using mean-variance mapping optimization

Kucuktezcan¹,

Genç²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a self-adaptive penalization technique and artificial neural networks to develop a fast and effective methodology.The proposed methodology is applied to a 16-generator 68-bus test system to solve the security-constrained optimization problem with both continuous and discrete decision variables. To find a proper and cost-effective solution for the control actions within an acceptable time, dynamic security assessment methodology based on artificial neural networks is integrated into the optimization process for predicting the violations of security constraints brought about by the candidate solutions. The proposed method effectively integrates a variety of popular heuristic optimization algorithms, including MVMO, differential evolution, particle swarm optimization, genetic algorithms, big bang-big crunch, and artificial bee colony. MVMO outperforms all the others in various aspects such as reliability and robustness.

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Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

“…The normalized values of the violations and objective functions are represented by v ′ (x) and f ′ (x), respectively [12].…”

Section: Constraints Of the Optimization Problem And Penalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic security enhancement of power systems using mean-variance mapping optimization

Kucuktezcan¹,

Genç²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…In Section 4 an analysis of trade-offs between exploration and exploitation is performed first, using various parameter sets. After that, series of comparison experiments on the set of 13 well known g benchmark functions are performed to verify the effectiveness of our proposed approach over the latest Karaboga and Akay's [14] ABC algorithm and other state-of-the-art algorithms [17], [18], [19].…”

Section: Our Improvementmentioning

confidence: 99%

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Bačanin¹,

Tuba²

2012

SIC

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Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay's ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering mean solutions.

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Plant‐wide optimization for gold hydrometallurgy based on the fuzzy qualitative model and interval number

Liu

Chang

Niu

et al. 2018

Journal of Chemometrics

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Since the difficulty of obtaining accurate online-measurement of some key variables in hydrometallurgy plant-wide production process causes that the quantitative models of some procedures are difficult to establish and the plant-wide optimization based on quantitative model is difficult to realize, a plant-wide optimization method based on interval numbers is proposed in this work. First, based on the information of expert knowledge and the experience of field workers, a fuzzy qualitative model is constructed, and outputs of the qualitative model are reasonably divided into multiple modes simultaneously. By analyzing the process properties, an optimal control problem for hydrometallurgy plant-wide production process in the steady state is proposed to achieve process requirements which is to obtain the lowest cost of sodium cyanide and zinc as well as high gold quality. Then, by using interval numbers to represent the key variables that cannot be measured, an optimization method based on interval numbers is proposed for every output mode of the qualitative model. Finally, the hydrometallurgy process is carried in industrial simulation experiment.The results demonstrate that the proposed optimization scheme has much wider applicability than conventional optimization methods, especially for its improved performance of solving optimization problem with uncertainty.KEYWORDS expert knowledge, hydrometallurgy, interval number, plant-wide optimization, qualitative model | INTRODUCTIONOptimal control is an effective way to realize fine control of the gold hydrometallurgy production process through improving its comprehensive production indicators, such as concentrate grade, metal recovery rate, mineral resources comprehensive utilization rate, etc. Furthermore, optimal control can achieve high yield and excellent quality through saving energy and reducing consumption. 1 Specifically, the gold hydrometallurgy plant-wide optimization has a great important significance in improving economic benefits and solving the problems of mineral resource shortage and underutilization. 2,3 Prior to implementing optimization and control, it is necessary to establish an accurate process model. Over the past several years, many modeling methods of hydrometallurgy process are based on quantitative mathematical models. [4][5][6] To realize the optimization of comprehensive production indicators in mineral processing, Yu et al establish a nonlinear multiobjective programming optimization model and use the improved evolutionary algorithm to optimize five production indicators. 7 To achieve comprehensive production indicator with high efficiency and low consumption in

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A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization

Cited by 183 publications

References 7 publications

Dynamic security enhancement of power systems using mean-variance mapping optimization

Dynamic security enhancement of power systems using mean-variance mapping optimization

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Plant‐wide optimization for gold hydrometallurgy based on the fuzzy qualitative model and interval number

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