Sonar inspired optimization (SIO) in engineering applications
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ORIGINAL PAPER
Sonar inspired optimization (SIO) in engineering applications Alexandros Tzanetos1 · Georgios Dounias1 Received: 5 January 2018 / Accepted: 13 August 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract Recently, a new Nature Inspired Intelligent scheme has been proposed and presented, named Sonar Inspired Optimization (SIO). This algorithm is inspired by the SONAR mechanism, which is used by Warships to detect targets and avoid mines. In this paper, improvements have been done to the SIO approach in an attempt to increase the performance of the algorithm. Also, results from experiments in known constrained Engineering applications are presented and discussed. SIO tackles with these problems, managing to overcome the performance of other Nature Inspired metaheuristics, heuristics and mathematical approaches in most of the cases. Keywords Sonar inspired optimization · Nature-Inspired Intelligent (NII) algorithm · Engineering optimization · Tension/ compression spring design · Welded beam design · Pressure vessel
1 Introduction In the last twenty (20) years, a growth on Nature Inspired Intelligent (NII) methods (Yang 2010; Chiong 2009; Liu and Tsui 2006) is observed. Applications (Marrow 2000) and new challenges (Yang 2012) are presented, underlying the major contribution of these algorithms on the field of optimization. Except for swarm based techniques (Kennedy et al. 2001), there are many others that are inspired by physical phenomena (Shah-Hosseini 2009) and laws of science (Nasir et al. 2012). Recently the authors have extensively searched and collected all the algorithms that are based in the abovementioned categories and extracted some useful conclusions (Tzanetos and Dounias 2017). The overwhelming majority is population based schemes. A detail that highlights the need of multiple agents to achieve high exploration, while many of these algorithms are based also on attraction between their agents through equations that model the main idea inspired from nature.
* Alexandros Tzanetos [email protected] Georgios Dounias [email protected] 1
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, University of the Aegean, 41 Kountouriotou Str., Chios 82121, Greece
Most of the schemes used, are based on the gravitational law [Gravitational Search Algorithm (Rashedi et al. 2009)] or in attraction-based laws, e.g. charged system search (Kaveh and Talatahari 2010), Electromagnetism-like optimization (Birbil and Fang 2003). Based on these phenomena, the best solution attracts all the others towards it. On the proposed scheme, introduced in (Tzanetos and Dounias 2017a, b), each agent doesn’t interact with the others and thus, performs its independent search. The only information shared between all agents is the best-so-far fitness achieved. That’s a very useful feature, because all best-so-far solutions are contributing to find the best one and the algorithm cannot be trapped in local optima. So, a good balance between explora
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