Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems
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Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems Fatma A. Hashim1 · Kashif Hussain2 · Essam H. Houssein3
· Mai S. Mabrouk4 · Walid Al-Atabany1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes’ Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC’17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris’ hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www. mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm Keywords Archimedes’ principle · Buoyant force · Optimization · Metaheuristic · Exploration and exploitation
1 Introduction Over the past few decades, the numerical optimization problems have become increasingly complex and require highly efficient methods to solve. For example, design cost problems in engineering and accuracy problems in data mining often demand methods to find optimum from a large number of available solutions, without wasting efforts in searching sub-optimal regions. Due to complex nature and highly non-convex landscapes, the search-space related to these Essam H. Houssein
[email protected] 1
Faculty of Engineering, Helwan University, Helwan, Egypt
2
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
3
Faculty of Computers and Information, Minia University, Minia, Egypt
4
Faculty of Engineering, Misr University for Science and Technology, 6th of October, Egypt
problems pose several challenges to optimization methods [1]. These include exceptionally complex modalities of the search environment; proportional to the problem size, as well as, growing problem dimensionality [2]. The conventional deterministic methods, based on simple ca
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