Differential Evolution Ant Colony Optimization (DEACO) technique in solving Economic Load Dispatch problem

Rahmat, Nur Ezan; Musirin, Ismail

doi:10.1109/peoco.2012.6230872

Cited by 23 publications

(11 citation statements)

References 10 publications

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“…In order to improve the performance of optimization algorithms, some authors have proposed hybrid algorithms [3,11,[16][17][18][25][26][27]. Hybridization is a technique that combines the characteristics of two (or more) different methods to derive the benefits of both methods and compensate any disadvantages that were suffered by both algorithms.…”

Section: Q DImentioning

confidence: 99%

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

Laouafi¹,

Boukadoum²,

Leulmi³

2018

TELKOMNIKA

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Section: Q DImentioning

confidence: 99%

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

Laouafi¹,

Boukadoum²,

Leulmi³

2018

TELKOMNIKA

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“…Untuk mengatasi masalah ini, beberapa metode heuristik telah terbukti keberhasilannya dalam menangani permasalahan ini yaitu constriction factor based particle swarm optimization (CFBPSO) dan kombinasi inertia weight contriction factor (IWCFPSO) [3], hybrid simulated annealing particle swarm optimization (SA-PSO) [4], multiple tabe search (MTS) [5], chaotic ant swarm optimization (CASO) [6], imperialist competitive algorithm (ICA) [7][8], extention particle swarm optimization (E-PSO) [9], differential evolution ant colony optimization (DE-ACO) [10], genetic algorithm (GA) [4]. Keunggulan dari metode GA terletak pada proses seleksi dan evaluasi yaitu crossover.…”

Section: Pendahuluanunclassified

“…Teknik optimasi gabungan untuk mengatasi konvergensi lokal yang dikenal dengan DEACO telah diuji pada kasus sistem tenaga IEEE 26 bus dan terbukti menghasilkan nilai minimum yang lebih baik [10]. Pembangkitan ekonomis dengan melakukan 2 pendekatan yaitu IWCFPSO.…”

Section: Studi Pustakaunclassified

Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

Kanata¹

2017

JRE

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“…Examples of these algorithms include the ant colony system (ACS), genetic algorithm (GA), particle swarm optimization (PSO), intelligent state space pruning (ISSP), and evolutionary computation (EC) [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

et al. 2017

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Abstract:The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately.

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Differential Evolution Ant Colony Optimization (DEACO) technique in solving Economic Load Dispatch problem

Cited by 23 publications

References 10 publications

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

Pembangkitan Ekonomis pada Unit Pembangkit Listrik Tenaga Diesel Telaga Gorontalo Menggunakan Algoritma Genetika

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

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