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Adaptive Central Force Optimization Algorithm Based on the Stability Analysis

Journal Article


Abstract


  • In order to enhance the convergence capability of the central force optimization (CFO) algorithm, an adaptive central force optimization (ACFO) algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.

Publication Date


  • 2015

Citation


  • Qian, W., Wang, B., & Feng, Z. (2015). Adaptive Central Force Optimization Algorithm Based on the Stability Analysis. Mathematical Problems in Engineering, 2015. doi:10.1155/2015/914789

Scopus Eid


  • 2-s2.0-84935830781

Web Of Science Accession Number


Volume


  • 2015

Abstract


  • In order to enhance the convergence capability of the central force optimization (CFO) algorithm, an adaptive central force optimization (ACFO) algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.

Publication Date


  • 2015

Citation


  • Qian, W., Wang, B., & Feng, Z. (2015). Adaptive Central Force Optimization Algorithm Based on the Stability Analysis. Mathematical Problems in Engineering, 2015. doi:10.1155/2015/914789

Scopus Eid


  • 2-s2.0-84935830781

Web Of Science Accession Number


Volume


  • 2015