FB-GSA: A fuzzy bi-level programming based gravitational search algorithm for unconstrained optimization
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FB-GSA: A fuzzy bi-level programming based gravitational search algorithm for unconstrained optimization Nitish Das1
· Aruna Priya P.1
Accepted: 14 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The Gravitational Search Algorithm (GSA) which is a prominent nature-inspired computing technique outperforms in the exploration stage, but its performance degrades in the exploitation stage. A fuzzy bi-level programming based gravitational search algorithm (FB-GSA) is proposed in this study. The basic concept to create FB-GSA is the iterative fuzzy decisionmaking operation. FB-GSA accompanies the algorithms such as Chaotic Gravitational Search Algorithm (CGSA), and the proposed local search using spectral Polak-Ribire-Polyak-3 (spectral PRP-3) method. Initially, the adaptive parameters, for the fuzzy decision-making process, are determined. Then, the controlled operation of constituent algorithms is executed using fuzzy Bi-level logic, which leads to an optimal solution. Experimental evaluation of FB-GSA is performed using several unimodal and multi-modal benchmark functions. Experimental results illustrate that FB-GSA outperforms other state-of-art works for most benchmarks. The simulation results for FB-GSA also presents a significant improvement in the convergence speed. The fuzzy-based adaptive control employed in FB-GSA makes it devoid of premature convergence. Keywords Nature-inspired computing · Exploration and exploitation · Chaotic gravitational search algorithm · Local search · Polak-ribire-polyak-3 method · Fuzzy bi-level programming
1 Introduction Nowadays, nature-inspired computing techniques have drawn the attention of the researchers to address real-world computing issues [1–3]. These techniques are incredibly fruitful to rectify complex problems in the field, such as smart grid design, communication network design, process control, etc., where exact solutions are difficult to determine [2, 4]. Most nature-inspired optimization techniques incorporate the following two mechanisms in their design: (a) an exploration mechanism, and (b) an exploitation mechanism [5, 6]. The exploration mechanism is termed for diversification of the solution points. It is responsible for
Aruna Priya P.
[email protected] Nitish Das [email protected] 1
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, Chennai, India
conducting a wide-spread search to identify new feasible solution regions. Hence, this mechanism reduces the probability of an optimization technique to stuck at local minima [6, 7]. On the other hand, the exploitation mechanism is referred for intensification of the solution points. It is accountable for performing a narrow-down search within the identified solution region. The main goal of this mechanism is to detect the best outcomes within a particular solution region [5, 7]. One of the nature-inspired computing methods is the gravitational search algorithm (GSA), which mimics the New
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