Although meta-heuristic optimization algorithms have been used to solve many optimization problems, they still suffer from two main difficulties: What are the best parameters for a particular problem? How do we escape from the local optima? In this paper, a new, efficient meta-heuristic optimization algorithm inspired by wild dog packs is proposed. The main idea involves using three self-compet...

Abstract In this paper we first show how to compute a combination of assets producing an appropriate index of past performance. The desired index is equal to Smax T = maxb ST (b) which is the maximum of T period investment return ST (b) = ∏T t=1 b · xt, where xi is the vector of returns for the tth investment period, and b is the portfolio vector specifying the fraction of wealth allocated to e...

In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of quadrotor. The attitude and position information quadrotor directly mapped PWM signals four rotors through neural network control. To constrain size policy updates, PPO Monte Carlo approximations optimal penalty coefficient. A optimiza...