Ye-Hoon Kim

Quantum-inspired Multiobjective Evolutionary Algorithm for Multiobjective 0/1 Knapsack Problems

This paper proposes a multiobjective evolutionary algorithm (MOEA) inspired by quantum computing, which is named quantum-inspired multiobjective evolutionary algorithm (QMEA). In the previous papers, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms for single-objective optimization problems. To improve the quality of the nondominated set as well as the diversity of population in multiobjective problems, QMEA is proposed by employing the concept and principles of quantum computing such as uncertainty, superposition, and interference. Experimental results pertaining to the multiobjective 0/1 knapsack problem show that QMEA finds solutions close to the Pareto-optimal front while maintaining a better spread of nondominated set.

Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the users preferences are incorporated for this purpose. This paper proposes preference-based solution selection algorithm (PSSA) by which user can select a preferred one out of nondominated solutions obtained by any one of MOEAs. The PSSA, which is a kind of multiple criteria decision making (MCDM) algorithm, represents users preference to multiple objectives or criteria as a degree of consideration by fuzzy measure and globally evaluates obtained solutions by fuzzy integral. The PSSA is also employed in each and every generation of evolutionary process to propose multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS). To demonstrate the effectiveness of PSSA and MQEA-PS, computer simulations and real experiments on evolutionary multiobjective optimization for the fuzzy path planner of mobile robot are carried out. Computer simulation and experiment results show that the users preference is properly reflected in the selected solution. Moreover, MQEA-PS shows improved performance for the DTLZ problems and fuzzy path planner optimization problem compared to MQEA with dominance-based selection and other MOEAs like NSGA-II and MOPBIL.

Evolutionary multi-objective optimization in robot soccer system for education

As the robot soccer system becomes stabilized, it has been used as an educational platform with which various topics on mobile robotics can be taught. As one of key topics in the education of mobile robotics is computational intelligence-based navigation, this paper proposes a multiobjective population-based incremental learning (MOPBIL) algorithm to obtain the fuzzy path planner for optimal path to the ball, minimizing three objectives such as elapsed time, heading direction and posture angle errors in a robot soccer system. MOPBIL employs the probabilistic mechanism, which generates new population using probability vectors. As the probability vectors are updated by referring to nondominated solutions, population converges to Pareto-optimal solution set. Simulation and experiment results show the effectiveness of the proposed MOPBIL from the viewpoint of the proximity to the Pareto-optimal set, size of the dominated space, coverage of two sets and diversity metric. By implementing each of the solutions into the educational platform, it can be educated how multi-objective optimization is realized in the real-world problem.

Evolutionary Multiobjective Footstep Planning for Humanoid Robots

This paper proposes a novel evolutionary multiobjective footstep planner for humanoid robots. First, a footstep planner using a univector field navigation method is proposed to provide a command state (CS), which is to be an input of a modifiable walking pattern generator (MWPG) at each footstep. Then, the MWPG generates corresponding trajectories for every leg joint of the humanoid robot at each footstep to follow the CS. Second, a multiobjective evolutionary algorithm (MOEA) is employed to optimize the univector fields satisfying multiple objectives in navigation. Finally, a preference-based selection algorithm based on a fuzzy measure and fuzzy integral is proposed to select the preferred one out of various nondominated solutions obtained by the MOEA. The effectiveness of the proposed evolutionary multiobjective footstep planner is demonstrated through computer simulations for a simulation model of a small-sized humanoid robot, HanSaRam-VIII.

Multiobjective quantum-inspired evolutionary algorithm for fuzzy path planning of mobile robot

This paper proposes a multiobjective quantum-inspired evolutionary algorithm (MQEA) to design efficient fuzzy path planner of mobil robot. MQEA employs the probabilistic mechanism inspired by the concept and principles of quantum computing. As the probabilistic individuals are updated by referring to nondominated solutions in the archive, population converges to Pareto-optimal solution set. In order to evaluate the performance of proposed MQEA, robot soccer system is utilized as a mobile robot system. Three objectives such as elapsed time, heading direction and posture angle errors are designed to obtain robust fuzzy path planner in the robot soccer system. Simulation results show the effectiveness of the proposed MQEA from the viewpoint of the proximity to the Pareto-optimal set. Moreover, various trajectories by the obtained solutions from the proposed MQEA are shown to verify the performance and to see its applicability.

Two-Layered Confabulation Architecture for an Artificial Creature's Behavior Selection

This paper proposes a novel two-layered confabulation architecture for an artificial creature to select a proper behavior considering the internally generated will and the context of the external environment consecutively. The architecture is composed of seven main modules for processing perception, internal state, context, memory, learning, behavior selection, and actuation. The two-layered confabulation in a behavior module is processed by a will-based confabulation and a context-based confabulation consecutively by referring to confabulation probabilities in a memory module. An arbiter in the behavior module chooses a proper behavior among the suggested ones from the two confabulations, which is to be put into an action. To demonstrate the effectiveness of the proposed architecture, experiments are carried out for an artificial creature, implemented in the 3-D virtual environment, which behaves as per its will considering the context in the environment.

Design and image-quality performance of high resolution CMOS-based X-ray imaging detectors for digital mammography

In digital X-ray imaging systems, X-ray imaging detectors based on scintillating screens with electronic devices such as charge-coupled devices (CCDs), thin-film transistors (TFT), complementary metal oxide semiconductor (CMOS) flat panel imagers have been introduced for general radiography, dental, mammography and non-destructive testing (NDT) applications. Re- cently, a large-area CMOS active-pixel sensor (APS) in combination with scintillation films has been widely used in a variety of digital X-ray imaging applications. We employed a scintillator- based CMOS APS image sensor for high-resolution mammography. In this work, both powder-type Gd2O2S:Tb and a columnar structured CsI:Tl scintillation screens with various thicknesses were fabricated and used as materials to convert X-ray into visible light. These scintillating screens were directly coupled to a CMOS flat panel imager with a 25 50 mm 2 active area and a 48 mm pixel pitch for high spatial resolution acquisition. We used a W/Al mammographic X-ray source with a 30 kVp energy condition. The imaging characterization of the X-ray detector was measured and analyzed in terms of linearity in incident X-ray dose, modulation transfer function (MTF), noise-power spectrum (NPS) and detective quantum efficiency (DQE).

Multi-objective quantum-inspired evolutionary algorithm-based optimal control of two-link inverted pendulum

This paper proposes a method to generate an optimal trajectory of nonlinear dynamical system and concurrently optimize multiple performance criteria. As the dimensionality of system increases, it is difficult to find values of cost/reward function of conventional optimal controllers. In order to solve this problem, the proposed method employs iterative linear quadratic regulator and multi-objective quantum-inspired evolutionary algorithm to generate various optimal trajectories that satisfy multiple performance criteria. Fuzzy measure and fuzzy integral are also employed for global evaluation by integrating the partial evaluation of each solution over criteria with respect to users degree of consideration for each criterion. Effectiveness of the proposed method is verified by computer simulation carried out for the problem of stabilizing two-link inverted pendulum model.

Software Robot in a PDA for Human Interaction and Seamless Service

In this paper, a new architecture is proposed for the efficient human interaction with software robot (Sobot) in a PDA and the Sobot transmission between PDAs. Sobot can move to any mobile device through a wireless communication network. This ability is required to follow its user by moving itself to a device in his/her new location. Sobot as an artificial creature, has genetic code which is a set of computerized codes representing the personality. It has an internal state which consists of motivation, homeostasis, and emotion. The data set of genetic code and current internal state are transmitted through the network when it moves to the other device. A main server manages IP addresses of registered devices, Sobots data set, and Sobots location for the transmission. To demonstrate the effectiveness of the proposed scheme, Sobot is implemented in windows mobile environment of PDA such that it can interact with a human being and move to the other mobile device without spatial limitation.

Multiobjective evolutionary optimization for tumor segmentation of breast ultrasound images

This paper proposes a robust multiobjective evolutionary algorithm (MOEA) to optimize parameters of tumor segmentation for ultrasound breast images. The proposed algorithm employs efficient schemes for reinforcing proximity to Pareto-optimal and diversity of solutions. They are designed to solve multiobjective problems for segmentation accuracy and speed. First objective is evaluated by difference between the segmented outline and ground truth. Second objective is evaluated by elapsed time during segmentation process. The experimental results show the effectiveness of the proposed algorithm compared with conventional MOEA from the viewpoint of proximity to the Pareto-optimal front (improved by 16.4% and 12.4%). Moreover, segmentation results of proposed algorithm describe faster segmentation speed (1.97 second) and higher accuracy (8% Jaccard).