Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Particle swarm optimization-based algorithms for TSP and generalized TSP
Information Processing Letters
An evolutionary game based particle swarm optimization algorithm
Journal of Computational and Applied Mathematics
A Feasible and Adaptive Water-Usage Prediction and Allocation Based on a Machine Learning Method
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
A Strategy for Resource Allocation and Pricing in Grid Environment Based on Economic Model
CINC '09 Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01
A multi-objective PSO for job-shop scheduling problems
Expert Systems with Applications: An International Journal
An evolutionary game-theoretical approach to particle swarm optimisation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Discrete Particle Swarm Optimization for Materials Budget Allocation in Academic Libraries
CSE '10 Proceedings of the 2010 13th IEEE International Conference on Computational Science and Engineering
Review: air traffic flow management with heuristic repair
The Knowledge Engineering Review
| Hi-index | 0.00 |
This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization DPSO method combined to Evolutionary Game Theory EGT. One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.