In the design of many mechanical systems, the human designer knows its topology and shape. The goal for computational design thus results in finding a parameter set that optimizes multiple design goals. This paper presents a novel optimization method for such parametric design, the process of which is based on the human design process, but the search algorithm of which is gradient-based for eff...

In this paper, we propose near admissible multiobjective search algorithms to approximate, with performance guarantee, the set of Pareto optimal solution paths in a state space graph. Approximation of Pareto optimality relies on the use of an epsilon-dominance relation between vectors, significantly narrowing the set of nondominated solutions. We establish correctness of the proposed algorithms...

This article studies a class of multirobot coordination problems where team robots aim to reach their goal regions with minimum time and avoid collisions obstacles other robots. A novel numerical algorithm is proposed identify the Pareto optimal solutions no robot can unilaterally reduce its traveling without extending others'. The consistent approximation in epigraphical profile sense guarante...

The growing demand for machine learning applications in industry has created a need fast and efficient methods to develop accurate models. Automated Machine Learning (AutoML) algorithms have emerged as promising solution this problem, designing models without the human expertise. Given effectiveness of neural network models, Neural Architecture Search (NAS) specialises their architectures auton...

This paper addresses a general multiobjective optimization problem. One of the most widely used methods of dealing with multiple conflicting objectives consists of constructing and optimizing a so-called achievement scalarizing function (ASF) which has an ability to produce any Pareto optimal or weakly/properly Pareto optimal solution. The ASF minimizes the distance from the reference point to ...