Transient search optimization: a new meta-heuristic optimization algorithm
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Transient search optimization: a new meta-heuristic optimization algorithm Mohammed H. Qais 1
&
Hany M. Hasanien 2 & Saad Alghuwainem 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This article offers a new physical-based meta-heuristic optimization algorithm, which is named Transient Search Optimization (TSO) algorithm. This algorithm is inspired by the transient behavior of switched electrical circuits that include storage elements such as inductance and capacitance. The exploration and exploitation of the TSO algorithm are verified by using twenty-three benchmark, where its statistical (average and standard deviation) results are compared with the most recent 15 optimization algorithms. Furthermore, the non-parametric sign test, p value test, execution time, and convergence curves proved the superiority of the TSO against other algorithms. Also, the TSO algorithm is applied for the optimal design of three well-known constrained engineering problems (coil spring, welded beam, and pressure vessel). In conclusion, the comparison revealed that the TSO is promising and very competitive algorithm for solving different engineering problems. Keywords Benchmark functions . Optimization methods . Transient search optimization algorithm;
1 Introduction The randomization concept encouraged the researchers to inspire different behaviors in nature and propose new metaheuristic algorithms to solve complicated mathematical problems [1]. The nature-inspiration can be a bio-inspiration or a physical-inspiration. The bio-inspiration is based on the life behavior of the creatures to locate their food and adapt their habitats. There are two types of bio-inspired algorithms: 1) swarm-based algorithms, which are imitating the group behavior of creatures’ society for searching and finding food sources; 2) evolutionary-based algorithms, which imitate the evolution concept of creatures. On the other hand, the physical-inspiration is based on scientific laws and equations
* Mohammed H. Qais [email protected] Hany M. Hasanien [email protected] Saad Alghuwainem [email protected] 1
Electrical Engineering Department, Faculty of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
2
Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
in many scientific disciplines such as chemistry, astronomy, electrical, disasters … etc. Mathematical equations of different sciences can be randomized in a special manner then tested and re-evaluated until reaching better results and applied to many engineering applications. The straightforwardness, strength, computational time, and suitable proposal are the general characteristics of the algorithms’ comparison and competition. In a literature review, tens of meta-heuristic algorithms were proposed in the last decades and applied to solve many engineering problems. In the swarm-based algorithms group [2], particle swarm optimization (PSO) is stimulated by the teeming deeds of the birds and fishes [3],
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