en Optimal design Optimal design پژوهشي Research <span style="font-size:11.5pt"><span style="text-justify:inter-ideograph"><span style="font-family:&quot;Times New Roman&quot;,serif">Although metaheuristic algorithms are popular tools for global optimization, none of them is reported as the best for all problems. Hybridization is an advanced solution to overcome the shortcomings of individual methods by using the power points of the others. Here, a popular swarm intelligent algorithm with high explorative capability is combined with an exploitative operator of differential evolution and some dynamic parameter variation, as well as a greedy operator to enhance the search refinement. The proposed method is evaluated on a variety of engineering and constrained engineering problems, including the optimal design of Belleville Spring, pressure vessel, car side impact problem, and Morrow point dam. According to the results, considerable improvement is observed with respect to the standard particle swarm optimizer as well as competitive performance with a number of metaheuristic algorithms.</span></span></span> Hybrid metaheuristic algorithms, differential evolution, particle swarm optimizer, mechanical engineering benchmarks, concrete dam, shape optimization 31 47 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-45&slc_lang=en&sid=1 M. Shahrouzi shahruzi@khu.ac.ir 180031947532846002889 180031947532846002889 Yes Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran Y. Naserifar 180031947532846002890 180031947532846002890 No Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran A. M. Taghavi 180031947532846002891 180031947532846002891 No Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran S.-Sh Emamzadeh 180031947532846002892 180031947532846002892 No Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran