2012 International Conference on Green Technologies (ICGT) 2012
DOI: 10.1109/icgt.2012.6477982
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Modified Artificial Bee Colony algorithm for non-convex economic dispatch problems

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Cited by 6 publications
(4 citation statements)
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“…3. Lebah penonton menunggu di sarang dan menonton tarian petunjuk lokasi sumber-sumber makanan yang menguntungkan dan memilih situs sumber makanan tergantung pada frekuensi tarian yang sebanding dengan kualitas sumbernya [6][7] [8]. Dalam algoritma ABC yang diusulkan oleh Karaboga, posisi sumber makanan merupakan solusi optimasi dan jumlah nektar sumber makanan merupakan profitabilitas (kebugaran) dari solusi yang terkait.…”
Section: A Artificial Bee Colonyunclassified
“…3. Lebah penonton menunggu di sarang dan menonton tarian petunjuk lokasi sumber-sumber makanan yang menguntungkan dan memilih situs sumber makanan tergantung pada frekuensi tarian yang sebanding dengan kualitas sumbernya [6][7] [8]. Dalam algoritma ABC yang diusulkan oleh Karaboga, posisi sumber makanan merupakan solusi optimasi dan jumlah nektar sumber makanan merupakan profitabilitas (kebugaran) dari solusi yang terkait.…”
Section: A Artificial Bee Colonyunclassified
“…Hence, more time is required to reach the global solution. The computer technology has been developed many new population based heuristic optimization techniques like differential evolution (DE) [8], evolutionary programming (EP) [9], hybrid evolutionary programming (HEP) [10], particle swarm optimization (PSO) [11], civilized swarm optimization (CSO) [12], craziness based PSO (CRPSO) [13], hybrid PSO (HPSO) [14], modified PSO (MPSO) [15], genetic algorithm (GA) [16], hybrid GA (HGA) [17], adaptive real coded GA (ARCGA) [18], bacteria foraging optimization (BFO) [19], modified BFO (MBFO) [20], modified artificial bee colony (ABC) [21], seeker optimization algorithm (SOA) [22], ant colony optimization (ACO) [23], tabu search (TS) [24], biogeography based optimization (BBO) [25], and quasi oppositional BBO (QOBBO) [26] , oppositional BBO (OBBO) [27], harmony search algorithm (HSA) [28] for solving ELD problems. Other optimization algorithms have been proposed to solve the ELD problem, like the opposition based harmony search algorithm (OHSA) which was introduced by Chatterjee et al [29].…”
Section: Introductionmentioning
confidence: 99%
“…So they prove to be a bit slow in their approach towards the global optimum solution. With the development in the computer technology, many new population based heuristic optimization techniques have been introduced, like differential evolution (DE) [8], hybrid evolutionary programming (HEP) [9], evolutionary programming (EP) [10], civilized swarm optimization (CSO) [11], particle swarm optimization (PSO) [12], craziness based PSO (CRPSO) [13], hybrid PSO (HPSO) [14], modified PSO (MPSO) [15], hybrid GA (HGA) [16], genetic algorithm (GA) [17],adaptive real coded GA (ARCGA) [18], bacteria foraging optimization (BFO) [19], modified artificial bee colony (ABC) [20], modified biogeography based optimization (BBO) [21], seeker optimization algorithm (SOA) [22], ant colony optimization (ACO) [23], tabu search (TS) [24], and quasi oppositional BBO (QOBBO) [25] , oppositional BBO (OBBO) [26], harmony search algorithm (HSA) [27] for solving ELD problems. Other optimization algorithms have been proposed to solve the ELD problem, like the opposition based harmony search algorithm (OHSA) which was introduced by [28].…”
Section: Introductionmentioning
confidence: 99%