A stability constrained adaptive alpha for gravitational search algorithm

Abstract

 Gravitational search algorithm (GSA), a recent meta-heuristic algorithm inspired by Newton's law of gravity  and mass interactions, shows good performance in various optimization problems. In GSA, the gravitational  constant attenuation factor alpha (α) plays a vital role in convergence and the balance between exploration and  exploitation. However, in GSA and most of its variants, all agents share the same α value without considering  their evolutionary states, which has inevitably caused the premature convergence and imbalance of exploration and  exploitation. In order to alleviate these drawbacks, in this paper, we propose a new variant of GSA, namely stability  constrained adaptive alpha for GSA (SCAA). In SCAA, each agent's evolutionary state is estimated, which is then  combined with the variation of the agent's position and fitness feedback to adaptively adjust the value of α.  Moreover, to preserve agents' stable trajectories and improve convergence precision, a boundary constraint is  derived from the stability conditions of GSA to restrict the value of α in each iteration. The performance of  SCAA has been evaluated by comparing with the original GSA and four alpha adjusting algorithms on 13    conventional functions and 15 complex CEC2015 functions. The experimental results have demonstrated that  SCAA has significantly better searching performance than its peers do.

Rulet Elektromanyetik Alan Optimizasyon (R-EFO) Algoritması

“…Bunlar deterministtik olmayan (tamamen rastgele gerçekleşen), deterministtik olan (tamamen uygunluk değerine bağlı olarak gerçekleşen) ve karma seçim yöntemleri (uygunluk değerini ve rastgeleliği dikkate alarak gerçekleşen) olarak özetlenebilir [18][19][20][21][22]. Genetik algoritma dışındaki MSA algoritmalarında seçim yöntemi olarak en çok deterministtik olan yöntem kullanılmaktadır [1][2][3][4][5][23][24][25][26][27][28][29][30]. Bu yöntemin uygulanışı topluluk içerisinde uygunluk değeri en yüksek olan çözüm adayının seçilmesidir.…”

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

“…However, most traditional training methods usually fail to achieve proper parameters of MLP, i.e., weights and biases [38,51]. Recently, we proposed a new GSA variant, SCAA, to discourage its premature convergence problem [61]. It could balance the tradeoff between exploration and exploitation search, and realize stable convergence of swarm agents.…”

“…Nevertheless, GSA still faces a premature convergence problem when processing complicated problems [56][57][58][59]. Thus, many GSA variants were proposed [56,60], including the stability-constrained adaptive alpha for the gravitational search algorithm (SCAA) [61], in which the searching performance of GSA was improved by adaptively adjusting the important parameter alpha.…”

Optimal Logistics Control of an Omnichannel Supply Chain

“…Here, the combination of linear and nonlinear feedbacks, i.e., the combined feedback, was selected to control the logistics of each node [21]. The stability of the combined feedback is well proven [22]. Further, the optimal control curve and endpoint value were solved, respectively, by the variational method and the virtual siphon method.…”