Ant colony optimization-based hybrid intelligent algorithms
نویسنده
چکیده
Ant colony optimization algorithm is a heuristic approach for the solution of combinatorial optimization problems. In order to solve continuous optimization models, an ant colony optimization algorithm is designed. Based on this algorithm, two hybrid intelligent algorithms combined with fuzzy simulation and neural network or integral sum approximation are introduced for solving fuzzy expected value models. Some numerical examples are given to illustrate the algorithms effective.
منابع مشابه
New Ant Colony Algorithm Method based on Mutation for FPGA Placement Problem
Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...
متن کاملFinding the Shortest Hamiltonian Path for Iranian Cities Using Hybrid Simulated Annealing and Ant Colony Optimization Algorithms
The traveling salesman problem is a well-known and important combinatorial optimization problem. The goal of this problem is to find the shortest Hamiltonian path that visits each city in a given list exactly once and then returns to the starting city. In this paper, for the first time, the shortest Hamiltonian path is achieved for 1071 Iranian cities. For solving this large-scale problem, tw...
متن کاملGradient-based Ant Colony Optimization for Continuous Spaces
A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...
متن کاملHVAC Control via Hybrid Intelligent Systems
A new hybrid intelligent system is proposed for Heating, Ventilation and Air Conditioning (HVAC) control by integration of Multiagent System (MAS), Dynamic Ontology (DO) and Ant Colony Optimization (ACO). The relevant combination between data driven and knowledge driven methods results in significant improvement of all behavioral indexes of HVAC control system – speed, stability, internal commu...
متن کاملThe multi-objective hybridization of particle swarm optimization and fuzzy ant colony optimization
In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOP...
متن کامل