Architectural generative design is an iterative technique that uses an algorithm to produce a small number of outputs to meet specific requirements. It is considered a recent methodology that has been successfully applied to solve various optimization problems in several domains of architecture and planning. It allows users to explore, optimize, and evaluate data-driven design alternatives based on project goals, constraints, and inputs. For the first time, engineers and designers can utilize new technologies offered by artificial intelligence to explore multiple design alternatives and weigh the trade-offs to identify improved design outcomes and make data-informed decisions faster than ever before.

The normal design process depends on the designer's mind. It's a never-ending cycle of redesign and review of each stage. Optimization has a complex effect on a design by finding a solution in various ways that innovate ideas, provide alternatives that can be adapted from parameters, and encourage the designer to see an open vision.

This paper aims to illustrate and design multiple alternatives that can assist designers in the field with innovative solutions and easier, faster work. which can mimic grasshoppers' natural feeding and swarming behaviors and is an intriguing swarm thanks to new technologies provided by an intelligence system.