A Hybrid Search algorithm for multicriteria optimization and evaluation in mechatronic products design

2009 ◽  
Author(s):  
Ouael Mouelhi ◽  
Pierre Couturier ◽  
Tanneguy Redarce
Keyword(s):  
Multicriteria Optimization ◽  
Search Algorithm ◽  
Hybrid Search

scholarly journals Reseach of Motor Energy Saving Method Based on Hybrid Search Algorithm

2019 ◽  
Vol 1176 ◽  
pp. 052028
Author(s):  
Song Chen ◽  
Dong Feng ◽  
Wenjing Li ◽  
Zhongcheng Wu
Keyword(s):  
Energy Saving ◽  
Search Algorithm ◽  
Hybrid Search

Movement Behavior for Soldier Agents on a Virtual Battlefield

2003 ◽  
Vol 12 (4) ◽  
pp. 387-410 ◽  
Author(s):  
Douglas A. Reece
Keyword(s):  
Search Algorithm ◽  
Human Movement ◽  
Movement Planning ◽  
Movement Behavior ◽  
Behavior Model ◽  
Topological Map ◽  
Hybrid Search ◽  
Individual Agent ◽  
Planning And Control ◽  
And Control

We have developed a movement behavior model for soldier agents who populate a virtual battlefield environment. Whereas many simulations have addressed human movement behavior before, none of them has comprehensively addressed realistic military movement at individual and unit levels. To design an appropriate movement behavior model, we found it necessary to elaborate all of the requirements on movement from the military tasks of interest, define a behavior architecture that encompasses all required movement tasks, select appropriate movement planning and control approaches in light of the requirements, and implement the planning and control algorithms with novel enhancements to achieve satisfactory results. The breadth of requirements in this problem domain makes simple behavior architectures inadequate and prevents any single planning approach from easily accomplishing all tasks. In our behavior architecture, a hierarchy of tasks is distributed over unit leaders and unit members. For movement planning, we use an A* search algorithm on a hybrid search space comprising a two-dimensional regular grid and a topological map; the plan produced is a series of waypoints annotated with posture and speed changes. Individuals control movement with reactive steering behaviors. The result is a system that can realistically plan and execute a variety of unit and individual agent movement tasks on a virtual battlefield.

Whole-Region Hybrid Search Algorithm for DSSS Signal Acquisition

2016 ◽  
Vol 95 (2) ◽  
pp. 1265-1284
Author(s):  
Yuyao Shen ◽  
Yongqing Wang ◽  
Xiuli Yu ◽  
Siliang Wu
Keyword(s):  
Search Algorithm ◽  
Signal Acquisition ◽  
Hybrid Search

scholarly journals A*+IDA*: A Simple Hybrid Search Algorithm

2019 ◽  
Author(s):  
Zhaoxing Bu ◽  
Richard E. Korf
Keyword(s):  
Search Algorithm ◽  
Original Version ◽  
Optimal Solutions ◽  
Hybrid Search ◽  
Rubik's Cube ◽  
Rubik’S Cube ◽  
Complex Algorithm ◽  
Simple Combination

We present a simple combination of A* and IDA*, which we call A*+IDA*. It runs A* until memory is almost exhausted, then runs IDA* below each frontier node without duplicate checking. It is widely believed that this algorithm is called MREC, but MREC is just IDA* with a transposition table. A*+IDA* is the first algorithm to run significantly faster than IDA* on the 24-Puzzle, by a factor of almost 5. A complex algorithm called dual search was reported to significantly outperform IDA* on the 24-Puzzle, but the original version does not. We made improvements to dual search and our version combined with A*+IDA* outperforms IDA* by a factor of 6.7 on the 24-Puzzle. Our disk-based A*+IDA* shows further improvement on several hard 24-Puzzle instances. We also found optimal solutions to a subset of random 27 and 29-Puzzle problems. A*+IDA* does not outperform IDA* on Rubik’s Cube, for reasons we explain.

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