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In this paper we propose a novel iterative search procedure for multi-objective optimization problems. The iteration process -though derivative free -utilizes the geometry of the directional cones of such optimization problems, and is capable both of moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. Next, we give one possible way of integrating this local search procedure into a given EMO algorithm resulting in a novel memetic strategy. Finally, we present some numerical results on some well-known benchmark problems indicating the strength of both the local search strategy as well as the new hybrid approach.

In this article, we introduce the command bsrwalkdrift, which is primarily intended to perform a bootstrap unit-root test under the null hypothesis of random walk with drift. The method implemented in this command is considerably more precise than the corresponding case of the conventional augmented Dickey–Fuller test, which can be inaccurate when the true value of the drift term is small relative to the standard deviation of the innovations. The command also has an option to account for deterministic linear trend and another option to perform bootstrap unit-root tests under the null hypothesis of random walk without drift.

The aim of our study was to investigate the effect of muscle-specific fatigue of the quadriceps and hamstring muscles on the biomechanical factors of anterior cruciate ligament (ACL) injury using musculoskeletal modeling techniques during directional diversion maneuver. Fifteen female subjects performed a directional diversion maneuver under three treatment conditions (quadriceps fatigue, hamstring fatigue, and control gait). Data from the 3D motion capture system and force platform were used to extract anterior/posterior ACL forces using the two-bundle ACL musculoskeletal modeling approach. A decrease in maximum extension (51.3%) and flexion (50.7%) torque after fatigue was observed. After quadriceps fatigue, the extension (p = 0.041) and adduction moments (p = 0.046) of the knee joint and the mean anterior bundle of ACL force (p = 0.021) decreased significantly. The knee flexion angle (p = 0.003), knee valgus angle (p = 0.013), and shear force (p = 0.043) decreased significantly after hamstring fatigue. The decrease in ACL force after quadriceps fatigue confirms its significant role in causing an ACL injury. However, no significant differences in ACL load after hamstring fatigue leads us to speculate that the antagonist muscle group, i.e., the hamstring, might not have a preventive mechanism against ACL injury.

Abstract. Multi-Objective Evolutionary Algorithms (MOEA) have been succesfully applied to solve control problems. However, many improvements are still to be accomplished. In this paper a new approach is proposed: the Multi-Objective Pole Placement with Evolutionary Algorithms (MOPPEA). The design method is based upon using complexvalued chromosomes that contain information about closed-loop poles, which are then placed through an output feedback controller. Specific cross-over and mutation operators were implemented in simple but efficient ways. The performance is tested on a mixed multi-objective H2/H∞ control problem.