Evolutionary bi-objective optimization of soil cutting by bull-dozer: A real-world application

Optimization is a procedure of finding an optimal solution from the feasible search space. In single-objective optimization, the solution gets improved iteratively based on the objective function value. But, most of the real-world problems involve more than one objective. In such situation, many solutions are optimal which are known as Pareto-optimal solutions. In this paper, we aim to solve one real-world optimization problem from the domain of construction equipment called as bull-dozer. We first formulate a bi-objective optimization problem with one constraint for soil cutting and pushing by the bull-dozer and then, solve it using genetic algorithm. We mainly target to perform the post-analysis of Pareto-optimal solutions which can evolve interesting relationships for the given problem.