2006Unpublished venueRequires access

Ant Direction Hybrid Differential Evolution for Solving Economic Dispatch of Power System

Sheng-Kuan Wang, Chih‐Wen Liu, Ji-Pyng Chiou

Open publisher page 14 citations

Abstract

This paper presents an ant direction hybrid differential evolution (ADHDE) method for solving the economic dispatch (ED) problem in power systems. The ADHDE utilizes the concept of an ant colony search to find a suitable mutation operator in the hybrid differential evolution (HDE) method, to accelerate the search for the global solution. Two economic dispatch problems, including the six and fifteen unit power systems, are applied to compare the performance of the proposed method with those of genetic algorithm (GAs) and simulated annealing (SA). Numerical results indicate that the proposed ADHDE method outperforms the SA and GA methods.

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What this paper is about

This paper presents an ant direction hybrid differential evolution (ADHDE) method for solving the economic dispatch (ED) problem in power systems. The ADHDE utilizes the concept of an ant colony search to find a suitable mutation operator in the hybrid differential evolution (HDE) method, to accelerate the search for the global solution. Two economic dispatch problems, including the six and fifteen unit power systems, are applied to compare the performance of the proposed method with those of genetic algorithm (GAs) and simulated annealing (SA). Numerical results indicate that the proposed ADHDE method outperforms the SA and GA methods.

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OpenAlex reports 14 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper presents an ant direction hybrid differential evolution (ADHDE) method for solving the economic dispatch (ED) problem in power systems. The ADHDE utilizes the concept of an ant colony search to find a suitable mutation operator in the hybrid differential evolution (HDE) method, to accelerate the search for the global solution. Two economic dispatch problems, including the six and fifteen unit power systems, are applied to compare the performance of the proposed method with those of genetic algorithm (GAs) and simulated annealing (SA). Numerical results indicate that the proposed ADHDE method outperforms the SA and GA methods.

Key concepts: Economic dispatch, Differential evolution, Simulated annealing, Ant colony optimization algorithms, Mathematical optimization, Computer science, Ant colony, Electric power system

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