Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamic vehicle routing using genetic algorithms
Applied Intelligence
A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery
Computers and Operations Research
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Online stochastic and robust optimization
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Flexible variable neighborhood search in dynamic vehicle routing
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems
The Journal of Supercomputing
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Many combinatorial real-world problems are mostly dynamic. They are dynamic in the sense that the global optimum location and its value change over the time, in contrast to static problems. The task of the optimization algorithm is to track this shifting optimum. Particle Swarm Optimization (PSO) has been previously used to solve continuous dynamic optimization problems, whereas only a few works have been proposed for combinatorial ones. One of the most interesting dynamic problems is the Dynamic Vehicle Routing Problem (DVRP). This paper presents a Multi-Adaptive Particle Swarm Optimization (MAPSO) for solving the Vehicle Routing Problem with Dynamic Requests (VRPDR). In this approach the population of particles is split into a set of interacting swarms. Such a multi-swarm helps to maintain population diversity and good tracking is achieved. The effectiveness of this approach is tested on a well-known set of benchmarks, and compared to other metaheuristics from literature. The experimental results show that our multiswarm optimizer significantly outperforms single solution and population based metaheuristics on this problem.