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We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer.

As robots get closer to humans, higher requests to robots are put forward. Human-like motion is one of those important issues, especially for humanoid service robots, advanced industrial robots and assistive robots. In this paper, a motion-decision algorithm is proposed and applied to human-like motion planning of robotic arms. The algorithm consists of two parts: intelligent decision and calculation of the joint trajectory. The former includes two parts: the hierarchical planning strategy (HPS) and the Bayesian decision. The HPS reflects the general rules of human arm movements and the robotic arms using the HPS can simulate the movements of human arms accurately. The Bayesian decision is used to make robotic arms choose an appropriate mode of motion. The calculation of the joint trajectory builds a motion framework of robotic arms to generate human-like movements. The human performance measures (HPMs) in different planning hierarchies are proposed. Finally, the validity of the proposed algorithm is verified by experiments.