en Applications Applications پژوهشي Research <span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span style="font-size:11.5pt"><span new="" roman="" style="font-family:" times=""><span style="color:#1f1f1f">This paper uses multi-objective methods to improve the exploration for single-objective problems. This method involved splitting the objective function into two segments and these enhanced using specialized algorithms designed for handling multiple objective functions. Three MO</span></span></span><span style="font-size:11.5pt"><span new="" roman="" style="font-family:" times=""> algorithms include Colliding Bodies Optimization<span style="color:#1f1f1f"> (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II), which are used to get the best prediction of structural modal strain energy. The indicated method is then implemented to two spatial truss structures. The recently developed method has superior performance over the previous approach that relied on single-objective optimization (SO) algorithms.</span></span></span></span></span></span> Neural networks, Strain energy, Optimization algorithms, Multi-objective optimizations, Truss structures 325 336 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-57&slc_lang=en&sid=1 V. R. Mahdavi v.r.mahdavi@arakut.ac.ir 180031947532846002998 180031947532846002998 Yes Department of Civil and Geomechanics Engineering, Arak University of Technology, Arak, Iran A. Kaveh alikaveh@iust.ac.ir 180031947532846002999 180031947532846002999 No School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran