The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays
Genetic Programming and Evolvable Machines
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies' behavior. This paper presents design and CMOS implementation of the ant colony optimization based algorithm for solving the TSP problem. In order to implement ant colony optimization algorithm in CMOS, we will present a new algorithm. This algorithm is based on the original ant colony optimization but it can be implemented in CMOS. Briefly, pheromone matrix is transformed on the chip area and ants move up-down through the pheromone matrix and they make their decisions. Finally ants select a global path. In previous researches only pheromone values is used, but select the next city in this paper is based on heuristics value and pheromone value. In definition of problem, we use heuristics value as a matrix. Previous researches could not be used for wide type of optimization problem but our chip gives heuristics value initially and we can change initial value of heuristics value according to the optimization problem so this capability increases the flexibility of ACO chip. Simple circuit is used in blocks of our chip to increase the speed of convergence of ACO chip. We use Linear Feedback Shift Register (LSFR) circuit for random number generator in ACO chip. ACO chip has capability of solving the big TSP problem. ACO chip is simulated by HSPICE software and simulation results show the good performance of final chip.