scholarly journals A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

10.5772/60624 ◽  
2015 ◽  
Vol 12 (7) ◽  
pp. 86 ◽  
Author(s):  
Mohammad Naim Rastgoo ◽  
Bahareh Nakisa ◽  
Mohd Zakree Ahmad Nazri
Keyword(s):  
Local Search ◽  
Search System ◽  
Modified Pso ◽  
Multi Robot

scholarly journals Iterated Local Search for Time-extended Multi-robot Task Allocation with Spatio-temporal and Capacity Constraints

2019 ◽  
Vol 28 (2) ◽  
pp. 347-360 ◽  
Author(s):  
Hakim Mitiche ◽  
Dalila Boughaci ◽  
Maria Gini
Keyword(s):  
Local Search ◽  
Task Allocation ◽  
Iterated Local Search ◽  
Spatial Constraints ◽  
Parameters Tuning ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Robot Task ◽  
Time Critical ◽  
Multi Robot

Abstract We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search that is suitable for time critical applications. We created test instances on which we experimentally show that our approach outperforms a state-of-the-art approach to a related problem. Our approach improves the baseline’s score on average by 2.35% and up to 10.53%, while responding in times shorter than the baseline’s, on average, 1.6 s and up to 5.5 s shorter. Furthermore, our approach is robust to run replication and is not very sensitive to parameters tuning.

Solution Attractor of Local Search System: A Method to Reduce Computational Complexity of the Traveling Salesman Problem

2020 ◽  
Author(s):  
Weiqi Li
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Traveling Salesman ◽  
Search System ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is presumably difficult to solve exactly using local search algorithms. It can be exactly solved by only one algorithm—the enumerative search algorithm. However, the scanning of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This chapter attempts to answer these questions. A local search algorithm is a discrete dynamical system, in which a search trajectory searches a part of the solution space and stops at a locally optimal point. A solution attractor of a local search system for the TSP is defined as a subset of the solution space that contains all locally optimal tours. The solution attractor concept gives us great insight into the computational complexity of the TSP. If we know where the solution attractor is located in the solution space, we simply completely search the solution attractor, rather than the entire solution space, to find the globally optimal tour. This chapter describes the solution attractor of local search system for the TSP and then presents a novel search system—the attractor-based search system—that can solve the TSP much efficiently with global optimality guarantee.

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