Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
A discrete particle swarm optimization algorithm for the generalized traveling salesman problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A discrete particle swarm optimization algorithm for uncapacitated facility location problem
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
An Improved Discrete Particle Swarm Optimization in Evacuation Planning
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A modified strategy for the constriction factor in particle swarm optimization
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
A multi-valued discrete particle swarm optimization for the evacuation vehicle routing problem
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
MAMECTIS/NOLASC/CONTROL/WAMUS'11 Proceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position A min-max approach is based on minimum capacity and maximum capacity of vehicles We analyzed the performance of the algorithms using evacuation datasets The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.