Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimationdistribution a...
详细信息
ISBN:
(纸本)9781424481262
Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.
This paper proposes a new biped gait optimization method based on estimation of distributionalgorithm (EDA). It is able to explicitly extract global statistical information from the selected solutions and build a pos...
详细信息
ISBN:
(纸本)0780393201
This paper proposes a new biped gait optimization method based on estimation of distributionalgorithm (EDA). It is able to explicitly extract global statistical information from the selected solutions and build a posterior probability distribution model of promising solutions based on the extracted information. Biped gait for a nine-link robot is firstly formulated as a multi-objective optimization problem with consideration of multi-constraints including balance and torque. Optimization parameters are angles at transition poses. Instead of searching the joint space directly, EDA is applied to estimate the probability distribution of each joint degree. By this means, inherent mapping relationship between joint coordinates and cost function can be described in term of probability density. Compared to common intelligent learning method, the proposed optimization method can formulate a proper and feasible combination of impulses by tuning less parameters and visiting less states. The effectiveness of the proposed EDA based biped gait optimization method has been tested on a soccer-playing humanoid robot named Robo-Erectus. Experiment results demonstrate that the learned trajectory makes a good balance between stability and energy cost in short learning epochs.
This article presents a robust EDA for global optimization with real parameters. The approach is based on the linear combination of individuals of two populations. One is the current population Pt, from which a probab...
详细信息
ISBN:
(纸本)9781605581309
This article presents a robust EDA for global optimization with real parameters. The approach is based on the linear combination of individuals of two populations. One is the current population Pt, from which a probability density model is created and a new population Ps is simulated. The new population Pt+1 is a linear combination of Pt and Ps. The linear combination factor involved is self-adaptive.
This paper summaries our recent work on combining estimation of distributionalgorithms （EDA） and other techniques for solving hard search and optimization problems： a） guided mutation, an offspring generator in w...
详细信息
This paper summaries our recent work on combining estimation of distributionalgorithms （EDA） and other techniques for solving hard search and optimization problems： a） guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem, b） evolutionary algorithms refining a heuristic, we advocate a strategy for solving a hard optimization problem with complicated data structure, and c） combination of two different local search techniques and EDA for numerical global optimization problems, its basic idea is that not all the new generated points are needed to be improved by an expensive local search.
A new biped gait generation and optimization method is proposed in the frame of estimation of distributionalgorithms (EDAs) with Q-learning method. By formulating the biped gait synthesis as a constrained multi-objec...
详细信息
ISBN:
(纸本)9781424402588
A new biped gait generation and optimization method is proposed in the frame of estimation of distributionalgorithms (EDAs) with Q-learning method. By formulating the biped gait synthesis as a constrained multi-objective optimization problem, a dynamically stable and low energy cost biped gait is generated by EDAs with Q-learning (EDA_Q), which estimate probability distributions derived from the objective function to be optimized to generate searching points in the highly-coupled and high dimensional working space of biped robots. To get the preferable permutation of the interrelated parameters, Q-learning is combined to build and modify the probability models in EDA autonomously. By making use of the global optimization capability of EDA, the proposed EDA_Q can also solve the local minima problem in traditional Q-learning. On the other hand, with the learning agent, EDA_Q can evaluate the probability distribution model regularly without pre-designed structure and updating rule. The simulation results show that faster and more accurate searching can be achieved to generate preferable biped gait. The gait has been successfully used to drive a soccer-playing humanoid robot called Robo-Erectus which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.
暂无评论