Ying-Tung Hsiao, Cheng-LongChuang and Cheng- Chih Chien, “Ant Colony Optimization for Best Path Planning,” International Symposium on Communications and Information Technologies 2004 (ISCIT 2004), Sapporo, Japan, 26-29 October 2004, pp. 109-113.

has been cited by the following article:

• TITLE: Differential Evolution Immunized Ant Colony Optimization Technique in Solving Economic Load Dispatch Problem

  AUTHORS: N. A. Rahmat, I. Musirin

  KEYWORDS: Ant Colony Optimization (ACO), Differential Evolution (DE), Differential Evolution Immunized Ant Colony Opti-mization (DEIANT)

  JOURNAL NAME: Engineering, Vol.5 No.1B, January 16, 2013

  ABSTRACT: Since the introduction of Ant Colony Optimization (ACO) technique in 1992, the algorithm starts to gain popularity due to its attractive features. However, several shortcomings such as slow convergence and stagnation motivate many researchers to stop further implementation of ACO. Therefore, in order to overcome these drawbacks, ACO is proposed to be combined with Differential Evolution (DE) and cloning process. This paper presents Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique in solving economic load dispatch problem. The combination creates a new algorithm that will be termed as Differential Evolution Immunized Ant Colony Optimization (DEIANT). DEIANT was utilized to optimize economic load dispatch problem. A comparison was made between DEIANT and classical ACO to evaluate the performance of the new algorithm. In realizing the effectiveness of the proposed technique, IEEE 57-Bus Reliable Test System (RTS) has been used as the test specimen. Results obtained from the study revealed that the proposed DEIANT has superior computation time.