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2021 ◽  
Author(s):  
Qi Zhang ◽  
Jiaqiao Hu

Many systems arising in applications from engineering design, manufacturing, and healthcare require the use of simulation optimization (SO) techniques to improve their performance. In “Actor-Critic–Like Stochastic Adaptive Search for Continuous Simulation Optimization,” Q. Zhang and J. Hu propose a randomized approach that integrates ideas from actor-critic reinforcement learning within a class of adaptive search algorithms for solving SO problems. The approach fully retains the previous simulation data and incorporates them into an approximation architecture to exploit knowledge of the objective function in searching for improved solutions. The authors provide a finite-time analysis for the method when only a single simulation observation is collected at each iteration. The method works well on a diverse set of benchmark problems and has the potential to yield good performance for complex problems using expensive simulation experiments for performance evaluation.


Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Eduardo Canale ◽  
Franco Robledo ◽  
Pablo Sartor ◽  
Luis Stábile

Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at the beginning of every term so that each student works with a different set of peers during every term, thus training his or her adaptation skills and expanding the peer network. Achieving diverse teams while avoiding–or minimizing—the repetition of student pairs is a complex and time-consuming task for MBA Directors. We introduce the Max-Diversity Orthogonal Regrouping (MDOR) problem to manage the challenge of splitting a group of people into teams several times, pursuing the goals of high diversity and few repetitions. We propose a hybrid Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Descent (GRASP/VND) heuristic combined with tabu search and path relinking for its resolution, as well as an Integer Linear Programming (ILP) formulation. We compare both approaches through a set of real MBA cohorts, and the results show that, in all cases, the heuristic approach significantly outperforms the ILP and manually formed teams in terms of both diversity and repetition levels.


2021 ◽  
pp. 116332
Author(s):  
Tao Zeng ◽  
Wenjun Wang ◽  
Hui Wang ◽  
Zhihua Cui ◽  
Feng Wang ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandian Du

Aiming at the problems of large tracking error and long tracking time in traditional multiperson target dynamic tracking methods, a new method based on wireless body area network for athlete training multiperson target dynamic tracking is proposed. First, the microinertial sensor in the wireless body area network is used to collect the multiperson image data of the athlete training, and the sparse representation is performed after processing, which improves the reliability of the data and reduces the tracking error. Secondly, the multiperson target dynamic tracking method based on the adaptive search box is used, combined with target isolation and occlusion detection, to judge the athlete’s training target. Finally, the nearest neighbor algorithm is used to construct an adaptive search box to achieve dynamic tracking of multiple targets. Experimental results show that this method can accurately measure the similarity of target features, with small tracking error and short tracking time. The minimum tracking error is only 0.11 frame.


2021 ◽  
Author(s):  
Li Chengbo ◽  
Wang Xiaoming ◽  
Huang Zixi ◽  
Pang Guangyao ◽  
Han Xinyu ◽  
...  

2021 ◽  
Vol 5 (4) ◽  
pp. 123
Author(s):  
Suppachai Chainarong ◽  
Rapeepan Pitakaso ◽  
Worapot Sirirak ◽  
Thanatkij Srichok ◽  
Surajet Khonjun ◽  
...  

This research presents a novel algorithm for finding the most promising parameters of friction stir welding to maximize the ultimate tensile strength (UTS) and maximum bending strength (MBS) of a butt joint made of the semi-solid material (SSM) ADC12 aluminum. The relevant welding parameters are rotational speed, welding speed, tool tilt, tool pin profile, and rotation. We used the multi-objective variable neighborhood strategy adaptive search (MOVaNSAS) to find the optimal parameters. We employed the D-optimal to find the regression model to predict for both objectives subjected to the given range of parameters. Afterward, we used MOVaNSAS to find the Pareto front of the objective functions, and TOPSIS to find the most promising set of parameters. The computational results show that the UTS and MBS of MOVaNSAS generate a 2.13% to 10.27% better solution than those of the genetic algorithm (GA), differential evolution algorithm (DE), and D-optimal solution. The optimal parameters obtained from MOVaNSAS were a rotation speed of 1469.44 rpm, a welding speed of 80.35 mm/min, a tool tilt of 1.01°, a cylindrical tool pin profile, and a clockwise rotational direction.


Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1805
Author(s):  
Suppachai Chainarong ◽  
Thanatkij Srichok ◽  
Rapeepan Pitakaso ◽  
Worapot Sirirak ◽  
Surajet Khonjun ◽  
...  

In this study, we present a new algorithm for finding the optimal friction stir welding parameters to maximize the tensile strength of a butt joint made of the semisolid material (SSM) ADC12 aluminum. The welding parameters were rotational speed, welding speed, tool tilt, tool pin profile, and rotational direction. The method presented is a variable neighborhood strategy adaptive search (VaNSAS) approach. The process of finding the optimal friction stir welding parameters comprises five steps: (1) identifying the type and range of friction stir parameters using a literature survey; (2) performing experiments according to (1); (3) constructing a regression model using the response surface method optimizer (RSM optimizer); (4) using VaNSAS to find the optimal parameters for the model obtained from (3); and (5) confirming the results from (4) using the parameter levels obtained from (4) to perform real experiments. The computational results revealed that the tensile strength generated from VaNSAS was 3.67% higher than the tensile strength obtained from the RSM optimizer parameters. The optimal parameters obtained from VaNSAS were a rotation speed of 2200 rpm, a welding speed of 108.34 mm/min, a tool tilt of 1.23 Deg, a tool pin profile of a hexagon, and a rotational direction of clockwise.


2021 ◽  
Author(s):  
Jan-Philipp Fränken ◽  
NIKOLAOS CHRISTOS THEODOROPOULOS ◽  
Neil R Bramley

We investigate the idea that human concept inference utilizes local incremental search within a compositional mental theory space. To explore this, we study judgments in a challenging task, where participants actively gather evidence about a symbolic rule governing the behavior of a simulated environment. Participants construct mini-experiments before making generalizations and explicit guesses about the hidden rule. They then collect additional evidence themselves (Experiment 1) or observe evidence gathered by someone else (Experiment 2) before revising their own generalizations and guesses. In each case, we focus on the relationship between participants’ initial and revised guesses about the hidden rule concept. We find an order effect whereby revised guesses are anchored to idiosyncratic elements of the earlier guesses. To explain this pattern, we develop a family of process accounts that combine program induction ideas with local (MCMC-like) adaptation mechanisms. A particularly local variant of this adaptive account captures participants’ revisions better than a range of alternatives. We take this as suggestive that people deal with the inherent complexity of concept inference partly through use of local adaptive search in a latent compositional theory space.


2021 ◽  
Vol 12 (4) ◽  
pp. 146-168
Author(s):  
Shiqi Wang ◽  
Zepeng Shen ◽  
Yao Peng

This paper proposes an algorithm named hybrid multi-population and adaptive search range strategy with particle swarm optimization (ARPSO) for solving multimodal optimization problems. The main idea of the algorithm is to divide the global search space into multiple sub-populations searching in parallel and independently. For diversity increasing, each sub-population will continuously change the search area adaptively according to whether there are local optimal solutions in its search space and the position of the global optimal solution, and in each iteration, the optimal solution in this area will be reserved. For the purpose of accelerating convergence, at the global and local levels, when the global optimal solution or local optimal solution is found, the global search space and local search space will shrink toward the optimal solution. Experiments show that ARPSO has unique advantages for solving multi-dimensional problems, especially problems with only one global optimal solution but multiple local optimal solutions.


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