Simple local search problems that are hard to solve
SIAM Journal on Computing
Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Solving Multiple Knapsack Problems by Cutting Planes
SIAM Journal on Optimization
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
Journal of Global Optimization
Hi-index | 0.00 |
The Ant Colony Optimization (ACO) algorithms are being applied successfully to a wide range of problems. ACO algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems (COPs). In this paper we investigate the influence of model bias in model-based search as ACO. We present the effect of two different pheromone models for ACO algorithm to tackle the Multiple Knapsack Problem (MKP). The MKP is a subset problem and can be seen as a general model for any kind of binary problems with positive coefficients. The results show the importance of the pheromone model to quality of the solutions.