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.
Highlights
Consider the following global optimization problem: max f (x)(1) subject to x ∈ Ω = {x ∈ RNd | Rmin ≤ x ≤ Rmax}, where f(x) : Ω ⊂ RNd → R is a real-valued bounded function and Rmin, Rmax, and x are Nd-dimensional continuous variable vectors
central force optimization (CFO), which was introduced by Formato in 2007 [9], is becoming a novel deterministic heuristic optimization algorithm based on gravitational kinematics
This version is almost identical to PR-CFO and compensates for the number of parameters that must be chosen by fixing a wide array of internal parameters at specific values [19]
Summary
CFO, which was introduced by Formato in 2007 [9], is becoming a novel deterministic heuristic optimization algorithm based on gravitational kinematics. Mahmoud proposed an efficient global hybrid optimization algorithm combining the CFO algorithm and the Nelder-Mead (NM) method in Mathematical Problems in Engineering [16]. This paper will further investigate the weight and gravitational constant settings by employing the geometry-velocity stability theory of discrete time-varying dynamic systems. Based on the stability analysis of the proposed algorithm, we explore the weight and gravitational constant settings.
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