Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem
Annals of Operations Research - Special issue on Tabu search
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
A dynamic vehicle routing problem with time-dependent travel times
Computers and Operations Research
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
An Improved Evolutionary Algorithm for Dynamic Vehicle Routing Problem with Time Windows
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Fast Multi-Swarm Optimization for Dynamic Optimization Problems
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
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In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.