An adaptive gravitational search algorithm for global optimization

Ying Ying Koay, Jian Ding Tan, Chin Wai Lim, Siaw Paw Koh, Sieh Kiong Tiong, Kharudin Ali

Research output: Contribution to journalArticle


Optimization algorithm has become one of the most studied branches in the fields of artificial intelligent and soft computing. Many powerful optimization algorithms with global search ability can be found in the literature. Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. In this research, an Adaptive Gravitational Search Algorithm (AGSA) is proposed. The AGSA is enhanced with an adaptive search step local search mechanism. The adaptive search step begins the search with relatively larger step size, and automatically fine-tunes the step size as iterations go. This enhancement grants the algorithm a more powerful exploitation ability, which in turn grants solutions with higher accuracies. The proposed AGSA was tested in a test suit with several well-established optimization test functions. The results showed that the proposed AGSA out-performed other algorithms such as conventional GSA and Genetic Algorithm in the benchmarking of speed and accuracy. It can thus be concluded that the proposed AGSA performs well in solving local and global optimization problems. Applications of the AGSA to solve practical engineering optimization problems can be considered in the future.

Original languageEnglish
Pages (from-to)724-729
Number of pages6
JournalIndonesian Journal of Electrical Engineering and Computer Science
Issue number2
Publication statusPublished - 01 Jan 2019


All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

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