Biologically inspired algorithms are becoming powerful in modern optimization. In this paper, the principles of a metaheuristic algorithm based on Harris hawks behavior are shown. The Harris Hawks Optimizer (HHO in short) was used for solving problems in applied mechanics (car side impact, cone clutch, three-dimensional beam and I beam optimization). In the end, a comparison of the results obtained by HHO and results obtained by other methods is given.
In the current research, problems in engineering are becoming more and more prominent. One of the classes of engineering problems in engineering design problems, where a set of variables is calibrated in order for the optimization function to have a minimal or maximal value. This function considers energy efficiency, cost efficiency, and production efficiency in engineering design. One of the ways such problems are solved is metaheuristics. In this paper, we demonstrate how Dingo Optimization Algorithm (DOA) can be used to solve certain optimization problems in mechanical engineering. Firstly, a brief review of the DOA and its biological inspiration is given, along with the most important formulae. The pseudo-code for this algorithm was written using MATLAB R2020a software suite. Dingo Optimization Algorithm (DOA) was used to optimize engineering problems, such as pressure vessel optimization, stepped cantilever beam, car side-impact, and cone clutch optimization. The results presented in this paper show that the DOA can produce relevant results in engineering design problems.
In the design of mechanical elements, designers usually consider certain objectives that are related with cost, time, quality and reliability of product, depending on the requirements. In this paper, parametric optimization of spring design problem, pressure vessel design problem, cantilever beam design problem, cone coupling design problem and welded beam design problem has been carried out using Particle Swarm Optimization (PSO for short). The pseudo code for this algorithm was written using Matlab R2018a software suite. Results of the PSO algorithm will be compared to results obtained by the Differential Evolution (DE), Modified Ant Colony Algorithm, (MACA), Grasshopper Optimization Algorithm (GOA), Water Cycle Algorithm (WCA), Cucko Search (CS) , Genetic Algorithm (GA), Ant Lion Optimization (ALO), Firefly Algorithm (FA) and Method of Moving Asymptotes (MMA), depending of solutions found in literature. The source code of Particle Swarm Optimization (PSO) algorithm is publicly available at https://seyedalimirjalili.com.
In this paper, the principles of a metaheuristic algorithm based on tunicate swarm behavior are shown. The Tunicate Swarm Algorithm (TSA for short) was used for solving problems in applied mechanics (speed reducer, cantilever beam and three-dimensional beam optimization). In the end, a comparison of results obtained by TSA and results obtained by other methods is given.
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