SOS 2.0: an evolutionary approach for SOS algorithm
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RESEARCH PAPER
SOS 2.0: an evolutionary approach for SOS algorithm Min‑Yuan Cheng1 · Richard Antoni Gosno1 Received: 8 March 2020 / Revised: 30 June 2020 / Accepted: 17 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract With the shortcomings on the solution given for most-recent optimization problems, decision-makers from different fields yearn the existence of tenacious breakthrough. In fact, they all shared the same obligation to optimize work efficiency, whether to minimize cost, consumption or to maximize the profit acquirement. Metaheuristic search is the more-advanced method proven to be useful for difficult optimization tasks. Moreover, development records also signalized rapid development of these algorithms, contributing several notable and powerful optimization algorithms. Among them, Symbiotic Organisms Search (SOS) received noticeable attention due to its simplicity and also its parameter-less nature. Nonetheless, several considerable issues are still challenging for further development. For instance, local optima and premature convergence issues found from any improper and inefficiency computational procedure on higher dimensional problems. Also, exploitation and exploration trade-off is another essential issue involving stability for optimal performance. In that case, this work proposed a new evolutionary approach named SOS 2.0. There are two distinct features associated with the evolution: Self-ParameterUpdating (SPU) technique and chaotic maps sequencing. Both features are integrated for a better balance of exploration and exploitation in which SPU focuses on exploration and chaotic map focuses on exploitation instead. This work also applied benchmarks function tests and engineering design optimization problem in advance for validation purpose of the performance. The experimental results showed that SOS 2.0 delivers not only better performance from its predecessor and also several recent SOS modifications which can be concluded as one successive approach for better SOS algorithm, but also enhances the computation efficiency and capability of searching optimal solution. Keywords Metaheuristics · Optimization · Symbiotic organisms search · Benchmark function · Engineering design optimization
1 Introduction In real-world practices, we often deal with numerous optimization problems. Almost all applications in engineering and industry share the same need to optimize something— whether to minimize the cost and energy consumption or to maximize the profit, output, performance, and efficiency [1]. While optimization problems are literally everywhere, the problems themselves are most complex and difficult to solve. Classical methods (numerical approach) are proven to * Min‑Yuan Cheng [email protected] Richard Antoni Gosno [email protected] 1
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, #43, Sec. 4, Keelung Rd., Taipei 106, Taiwan, ROC
be useful in finding the optimum solution for several optimization
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