Minh-Trien Pham

A Global Optimization Algorithm for Electromagnetic Devices by Combining Adaptive Taylor Kriging and Particle Swarm Optimization

This paper presents an efficient optimization strategy which employs adaptive Taylor Kriging and Particle Swarm Optimization (PSO). In this method, the objective function of electromagnetic problem is interpolated by using adaptive Taylor Kriging, in which the covariance parameter is obtained by Maximum Likelihood Estimation (MLE). And then, PSO is used to search for optimal solutions of electromagnetic problem. The proposed algorithm is verified its validity by analytic functions and TEAM (Testing of Electromagnetic Analysis Method) problem 22.

Robust Global Optimization of Electromagnetic Devices With Uncertain Design Parameters: Comparison of the Worst Case Optimization Methods and Multiobjective Optimization Approach Using Gradient Index

The uncertainties in design variables are unavoidable in the optimal design of electromagnetic devices, and there is an imperative demand to find a robust design, which is insensitive to the uncertainties and remains within the feasible region of constraints even perturbed by the uncertainties. In this paper, a gradient-based worst case optimization (G-WCO) algorithm is proposed in a limited uncertainty set to increase the numerical efficiency based on the worst case optimization (WCO) algorithm. Through applications to the robust optimal design of TEAM 22, the performances of the proposed G-WCO, conventional WCO, and multiobjective optimization approach using gradient index (GI) are compared.

Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems

The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22.

A Kangaroo inspired heterogeneous swarm of mobile robots with global network integrity for fast deployment and exploration in large scale structured environments

A heterogeneous robotic swarm for fast deployment and exploration in large scale structured environments is addressed in this paper. The swarm consists of a marsupial robot that is capable of carrying the small robots for fast deployment and loading the small robots for fast displacement. The heterogeneous robotic swarm is governed by a hierarchical distributed control including distributed node control for behavioural control and connectivity maintenance, and distributed connectivity control for network expansion and global network integrity. We illustrate systematic characteristics and potential applications of the heterogeneous robotic swarm compared to the homogeneous robotic swarm through simulation results.

Self-deployment strategy for a swarm of robots with global network preservation to assist rescuers in hazardous environments

In this paper, we present a self-deployment strategy for a swarm of robots that is capable of exploring and identifying victims in an unknown structured building environment while preserving a global network interConnectivity for information exchange. The strategy are conducted in two phases: self-displacement shifting the robotic swarm from room to room, and self-dispersion and aggregation for exploration and coverage in each room. A decentralised control is built up by decentralised node control governing the dispersion and aggregation and decentralised connectivity control guaranteeing the global network preservation. The self-deployment strategy reduces significantly number of the robots while increasing its capacity of exploration and coverage. The simulation results illustrate technical aspects of the robotic swarm with the application of exploration, search and rescue services.