Very recently, the first mathematical runtime analyses for NSGA-II, most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that NSGA-II optimizes OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, time such an advantage of proven ...

Nowadays, the subject of vision metrology network design is local enhancement of the existing network. In the other words, it has changed from first to third order design concept. To improve the network, locally, some new camera stations should be added to the network in drawback areas. The accuracy of weak points is enhanced by the new images, if the related vision constraints are satisfied si...

The evolutionary approach in the design optimisation of MEMS is a novel and promising research area. The problem is of a multi-objective nature; hence, multi-objective evolutionary algorithms (MOEA) are used. The literature shows that two main classes of MOEA have been used in MEMS evolutionary design Optimisation, NSGA-II and MOGA-II. However, no one has provided a justification for using eith...

This study proposes a bi-objective model for capacitated multi-vehicle allocation of customers to potential distribution centers (DCs).The optimization objectives are to minimize transit time and total cost including opening cost, assumed for opening potential DCs and shipping cost from DCs to the customers where considering heterogeneous vehicles lead to a more realistic model and cause more c...

The Protein Structure Prediction (PSP) problem is concerned about the prediction of the native tertiary structure of a protein in respect to its amino acids sequence. PSP is a challenging and computationally open problem. Therefore, several researches and methodologies have been developed for it. In this way, developers are working to integrate frameworks in order to improve their capabilities ...

Multi-objective evolutionary algorithms rely on the use of variation operators as their basic mechanism to carry out the evolutionary process. These operators are usually fixed and applied in the same way during algorithm execution, e.g., the mutation probability in genetic algorithms. This paper analyses whether a more dynamic approach combining different operators with variable application ra...

A multi-objective optimization problem (MOP) is often found in real-world optimization problem. Among various multiobjective optimization techniques, multi-objective evolutionary algorithm (MOEA) is highlighted as a good candidate due to its flexibility, feasibility, and its ability to handle multiple solutions. Among various MOEAs, we analyze 2MOEA which can achieve good convergence and divers...

-In this paper, a fast dynamical multi-objective evolutionary algorithm (DMOEA) based of the principle of the minimal free energy in thermodynamics, was developed to solve mechanical component design problems. Its performance was compared with the ε -constraint method and NSGA-II proposed by Deb et al. Simulation results show that the proposed evolutionary approach produces excellent solutions ...

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. Multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multipl...