en Applications Applications پژوهشي Research <span style="font-size:11.5pt"><span new="" roman="" style="font-family:" times="">This study aimed to develop and optimize artificial stone mix designs incorporating microsilica using artificial neural networks (ANNs) and metaheuristic optimization algorithms. Initially, 10 base mix designs were prepared and tested based on previous experience and literature. The test results were used to train an ANN model. The trained ANN was then optimized using SA-EVPS and EVPS algorithms to maximize 28-day compressive strength, with aggregate gradation as the optimization variable. The optimized mixes were produced and tested experimentally, revealing some discrepancies with the ANN predictions. The ANN was retrained using the original and new experimental data, and the optimization process was repeated iteratively until an&nbsp;acceptable agreement was achieved between predicted and measured strengths. This approach demonstrates the potential of combining ANNs and metaheuristic algorithms to efficiently optimize artificial stone mix designs, reducing the need for extensive physical testing.</span></span> Artificial stone Microsilica, Mix design optimization, Artificial neural networks, Metaheuristic algorithms, Enhanced Vibrating Particles System (EVPS), Self-Adaptive Enhanced Vibrating Particles System (SA-EVPS) 445 460 http://ijoce.iust.ac.ir/browse.php?a_code=A-10-6463-6&slc_lang=en&sid=1 P. Hosseini p.hosseini@mahallat.ac.ir 180031947532846002528 180031947532846002528 Yes Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran A. Kaveh 180031947532846002529 180031947532846002529 No School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran A. Naghian 180031947532846002530 180031947532846002530 No Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran A. Abedi 180031947532846002531 180031947532846002531 No Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran