Ki-Baek Lee

Multiobjective Particle Swarm Optimization With Preference-Based Sort and Its Application to Path Following Footstep Optimization for Humanoid Robots

This paper proposes multiobjective particle swarm optimization with preference-based sort (MOPSO-PS), in which the users preference is incorporated into the particle swarm optimization (PSO) update process to determine the relative merits of nondominated solutions while handling the mutual dependences and priorities of objectives. In MOPSO-PS, the users preference is represented as the degree of consideration for each objective using the fuzzy measure. The global evaluation of a particle, which represents the quality of the particle according to the users preference, is carried out by the fuzzy integral, which integrates the partial evaluation value of each objective with respect to the degree of consideration. Since the global best attractor of each particle in the population is randomly chosen among the nondominated particles having a relatively higher global evaluation value in each PSO update iteration, the optimization is gradually guided by the users preference. After the optimization, the most preferable particle can be chosen for practical use by selecting the particle with the highest global evaluation value. The effectiveness of the proposed MOPSO-PS is demonstrated by the application of path, following footstep optimization for humanoid robots in addition to empirical comparison with the other algorithms. The footsteps optimized by the MOPSO-PS were verified by simulation. The results indicate that the users preference is properly reflected in optimized solutions without any loss of overall solution quality or diversity.

Particle Swarm Optimization driven by Evolving Elite Group

This paper proposes a novel hybrid algorithm of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), named Particle Swarm Optimization driven by Evolving Elite Group (PSO-EEG) algorithm. The hybrid algorithm combines the movement update property of canonical PSO with the evolutionary characteristics of EP. It is processed in two stages; elite group stage by EP and ordinary group stage by PSO. For the former group, a novel concept of Evolving Elite Group (EEG) is introduced, which consists of relatively superior particles in a population. The elite particles are evolved by mutation and selection scheme of EP. The other ordinary particles refer to the closest elite particle as well as the global best and the personal best, to update their location. Simulation results demonstrate the proposed PSO-EEG is highly competitive in terms of robustness, accuracy and convergence speed for five well-known complex test functions.

Multi-objective particle swarm optimization with preference-based sorting

Multi-objective particle swarm optimization (MOPSO) provides a set of nondominated solutions and the number of nondominated solutions increases exponentially when the number of objectives increases. To select a desired solution out of them, preference-based solution selection algorithm (PSSA) was proposed by incorporating users preference into multi-objective evolutionary algorithms. In this paper, multi-objective particle swarm optimization with preference-based sorting (MOPSO-PS) is proposed, where a global best position is randomly selected from the archive of nondominated solutions sorted by global evaluation considering users preferences for multiple objectives. The users preference is represented as a degree of consideration for the objectives by the fuzzy measures. The global evaluation of the solutions is carried out by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation of each solution is obtained as a normalized objective function value. To demonstrate the effectiveness of the proposed MOPSO-PS, empirical comparisons to NSGA-II, MQEA, and MOPSO are carried out for the DTLZ functions. Experimental results show that the users preference is properly reflected in the selected solutions without any loss of overall quality and diversity.

DMOPSO: Dual multi-objective particle swarm optimization

Since multi-objective optimization algorithms (MOEAs) have to find exponentially increasing number of nondominated solutions with the increasing number of objectives, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. This paper proposes dual multi-objective particle swarm optimization (DMOSPO) by introducing secondary objectives of maximizing both user preference and diversity to the nondominated solutions obtained for primary objectives. The proposed DMOSPO can induce the balanced exploration of the particles in terms of user preference and diversity through the dual-stage of nondominated sorting such that it can generate preferable and diverse nondominated solutions. To demonstrate the effectiveness of the proposed DMOPSO, empirical comparisons with other state-of-the-art algorithms are carried out for benchmark functions. Experimental results show that DMOPSO is competitive with the other compared algorithms and properly reflects the users preference in the optimization process while maintaining the diversity and solution quality.

Improved version of a multiobjective quantum-inspired evolutionary algorithm with preference-based selection

Multiobjective quantum-inspired evolutionary algorithm (MQEA) employs Q-bit individuals, which are updated using rotation gate by referring to nondominated solutions in an archive. In this way, a population can quickly converge to the Pareto optimal solution set. To obtain the specific solutions based on users preference in the population, MQEA with preference-based selection (MQEA-PS) is developed. In this paper, an improved version of MQEA-PS, MQEA-PS2, is proposed, where global population is sorted and divided into groups, upper half of individuals in each group are selected by global evaluation, and selected solutions are globally migrated. The global evaluation of nondominated solutions is performed by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation value is obtained from a normalized objective function value. To demonstrate the effectiveness of the proposed MQEA-PS2, comparisons with MQEA and MQEA-PS are carried out for DTLZ functions.

Does External Knowledge Sourcing Enhance Market Performance? Evidence from the Korean Manufacturing Industry

Firms continuously search for external knowledge that can contribute to product innovation, which may ultimately increase market performance. The relationship between external knowledge sourcing and market performance is not well-documented. The extant literature primarily examines the causal relationship between external knowledge sources and product innovation performance or to identify factors which moderates the relationship between external knowledge sourcing and product innovation. Non-technological innovations, such as organization and marketing innovations, intervene in the process of external knowledge sourcing to product innovation to market performance but has not been extensively examined. This study addresses two research questions: does external knowledge sourcing lead to market performance and how does external knowledge sourcing interact with a firm’s different innovation activities to enhance market performance. This study proposes a comprehensive model to capture the causal mechanism from external knowledge sourcing to market performance. The research model was tested using survey data from manufacturing firms in South Korea and the results demonstrate a strong statistical relationship in the path of external knowledge sourcing (EKS) to product innovation performance (PIP) to market performance (MP). Organizational innovation is an antecedent to EKS while marketing innovation is a consequence of EKS, which significantly influences PIP and MP. The results imply that any potential EKS effort should also consider organizational innovations which may ultimately enhance market performance. Theoretical and practical implications are discussed as well as concluding remarks.

Multi-objective evolutionary algorithm-based optimal posture control of humanoid robots

This paper proposes a multi-objective evolutionary algorithm-based optimal posture controller to generate an optimal trajectory of humanoid robots against external disturbance using an iterative linear quadratic regulator (ILQR) and concurrently optimize multiple performance criteria. As the dimensionality of nonlinear system increases, it is difficult to find the weighting matrices of cost function in ILQR. In the proposed method, this problem is solved by employing a multi-objective quantum-inspired evolutionary algorithm (MQEA) to obtain nondominated solutions of the weighting matrices generating various optimal trajectories that satisfy multiple performance criteria. Among numerous nondominated solutions generated from MQEA, fuzzy measure and fuzzy integral are employed for global evaluation by integrating the partial evaluation of each of them over criteria with respect to users degree of consideration for each criterion. The effectiveness of the proposed method is verified by computer simulations for the problem of balancing the posture of a humanoid robot against external impulse force, where the robot is modeled by a four-link inverted pendulum.

Walking Pattern Generator Using an Evolutionary Central Pattern Generator

For the generation of locomotion, such as walking, running or swimming, vertebrate and invertebrate animals use the Central PatternGenerator (CPG). In this paper, a walking pattern generator is proposed using an evolutionary optimized CPG. Sensory feedback pathways in CPG are proposed, which uses Force Sensing Resistor (FSR) signals. For the optimization of CPG parameters, quantuminspired evolutionary algorithm is employed. Walking pattern generator is developed to generate trajectories of ankles and hip using CPG. The effectiveness of the proposed scheme is demonstrated by simulations and real experiments using a Webot dynamic simulator and a small sized humanoid robot, HSR-IX.

Reflex and Emotion-based Behavior Selection for Toy Robot

This paper presents a robotic doll with emotional and reflexive behaviors. The robotic doll imitates an animals appearance to provide comfort in human interaction. Emotion is considered to show more natural behaviors and to interact with user more intimately. Behavior is selected based on reflex-ness and emotion. A bear-like robotic doll, GomDoll is developed with the full use of the available degrees of freedom, sensors, and emotional and reflexive architecture implemented on a micro-controller. Experimental results demonstrate the effectiveness of the proposed architecture.

Dynamic Simulation of Modifiable Walking Pattern Generation to Handle Infeasible Navigational Commands for Humanoid Robots

The modifiable walking pattern generation (MWPG) algorithm can handle dynamic walking commands by changing the walking period, step length, and direction independently. When an infeasible command is given, the algorithm changes the command to a feasible one. After the feasibility of the navigational command is checked, it is translated into the desired center of mass (CM) state. To achieve the desired CM state, a reference CM trajectory is generated using predefined zero moment point (ZMP) functions. Based on the proposed algorithm, various complex walking patterns were generated, including backward and sideways walking. The effectiveness of the patterns was verified in dynamic simulations using the Webots simulator.