Optimization of economic load dispatch problems using biogeography based optimization technique

Singh, Jitendra; Goyal, Sunil Kumar

doi:10.1109/energyeconomics.2015.7235067

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Jitendra Singha et. al proposed above mentioned algorithm based on environmental distribution of species [8] for solution of economic load scheduling approach. Recently, Mirjalili et al [9] proposed a new meta-heuristic technique called grey wolf optimization based on leadership ladder and hunting process of the grey wolves.…”

Section: Introductionmentioning

confidence: 99%

Solution of Economically Load Scheduling Problem using Teaching Learning and Bio-Geography Based Hybrid Optimization Algorithm

Maity*¹,

Banerjee²,

Chanda³

2019

IJRTE

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The article presents a new optimization algorithm view namely hybridization of teaching learning (TLBO) and biogeography based (BBO) optimization algorithm used to solve the convex economic dispatch (ED) problem with non-linear constraints like ramp rate limit, valve point loading effect etc. Hybridization of TLBO and BBO is the mixed combination of superior properties of TLBO and BBO. Teaching learning algorithm (TLBO) is based on teacher learner relationship in class and bio-geography algorithm (BBO) is based on geographical representation of biological species. The main goal of ED is to allocate power allocation economically meeting load demand. The proposed algorithm is tested for 13-unit, 15-unit, 40-unit and 140 unit systems. For proving superiority properties of proposed algorithm, obtained result are compared with recent algorithm. It gives optimum fuel cost compared to other optimization algorithm.

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Section: Introductionmentioning

confidence: 99%