Open Access

ARTICLE

SSABA: Search Step Adjustment Based Algorithm

by Fatemeh Ahmadi Zeidabadi¹, Ali Dehghani², Mohammad Dehghani³, Zeinab Montazeri⁴, Štěpán Hubálovský⁵, Pavel Trojovský^3,*, Gaurav Dhiman⁶

1 Department of Mathematics and Computer Sciences, Sirjan University of Technology, Sirjan, Iran
2 Department of Civil Engineering, Islamic Azad University, Estahban Branch, Estahban, Iran
3 Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, 500 03, Czech Republic
4 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
5 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Králové, 500 03, Czech Republic
6 Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, India

* Corresponding Author: Pavel Trojovský. Email:

(This article belongs to the Special Issue: AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems)

Computers, Materials & Continua 2022, 71(3), 4237-4256. https://doi.org/10.32604/cmc.2022.023682

Received 17 September 2021; Accepted 20 October 2021; Issue published 14 January 2022

Abstract

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

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Keywords

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