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APPLICATION OF GENETIC ALGORITHM/TABU SEARCH COMBINATION ALGORITHM IN DISTRIBUTION NETWORK REAL-TIME PLANNING

Hong Lu

Open publisher page 4 citations

Abstract

Analyzing and comparing the advantage and shortcomings of Genetic Algorithm (GA) and Tabu Search (TS), a new hybrid algorithm GA/TS with the advantages of both GA and TS is led in distribution network capacitor real time optimal planning which is a problem of being non linear, combinatorial constrained and requiring quick solution. The test results from some sampling systems prove the feasibility and efficiency of the new hybridized algorithm and it is indicated that this algorithm is potential to be used for on line operation.

About this research paper

What this paper is about

Analyzing and comparing the advantage and shortcomings of Genetic Algorithm (GA) and Tabu Search (TS), a new hybrid algorithm GA/TS with the advantages of both GA and TS is led in distribution network capacitor real time optimal planning which is a problem of being non linear, combinatorial constrained and requiring quick solution. The test results from some sampling systems prove the feasibility and efficiency of the new hybridized algorithm and it is indicated that this algorithm is potential to be used for on line operation.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Analyzing and comparing the advantage and shortcomings of Genetic Algorithm (GA) and Tabu Search (TS), a new hybrid algorithm GA/TS with the advantages of both GA and TS is led in distribution network capacitor real time optimal planning which is a problem of being non linear, combinatorial constrained and requiring quick solution. The test results from some sampling systems prove the feasibility and efficiency of the new hybridized algorithm and it is indicated that this algorithm is potential to be used for on line operation.

Key concepts: Tabu search, Algorithm, Genetic algorithm, Computer science, Estimation of distribution algorithm, Population-based incremental learning, Line (geometry), Mathematical optimization

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