Economic dispatch problem is an optimization problem where objective function is highly non linear, non-convex, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper presents a comparative study of four different evolutionary algorithms i.e. genetic algorithm, bacteria foraging optimizatio...

This paper presents an hybrid algorithm based on genetic algorithm and ant colony optimization for continuous optimization, which combines the global exploration ability of the former with the local exploiting ability of the later. The proposed algorithm is evaluated on several benchmark functions. The simulation results show that the proposed algorithm performs quite well and outperforms class...

As the sports training predicting model based on chaos local predicting method still has the low predicting accuracy and the slow function speed problems, this paper proposes a sports training chaos predicting model based on weight control ant colony algorithm. It firstly uses the comprehensive weight factor to perform the weight control to the initial information of the new join node in the an...

An algorithm for edge detection with low contrast image using improved ant colony optimization is proposed in this paper. In edge detection, some parts of the edge are disappeared or disconnected because of low contrast in their intensities. This paper concentrates to this low-contrast problem. To detect these pixels, the direction tendency of edge lines is computed and ant colony optimization ...

This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman problems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structur...

This study proposes an Ant Colony Optimization using Genetic Information (GIACO). The GIACO algorithm combines Ant Colony Optimization (ACO) with Genetic Algorithm (GA). GIACO searches solutions by using the pheromone of ACO and the genetic information of GA. In addition, two kinds of ants coexist: intelligent ant and dull ant. The dull ant is caused by the mutation and cannot trail the pheromo...

through a collection of cooperative agents called ants, the near optimal solution to the multi-reservoir operation problem may be effectively achieved employing ant colony optimization algorithms (acoas). the problem is approached by considering a finite operating horizon, classifying the possible releases from the reservoir(s) into pre-determined intervals, and projecting the problem on a grap...

Swarm robotic is a new research area in the domain of Artificial intelligence. Particularly, the swarm robot concept is adopted from Mother Nature that combines small robots in a group to solve a particular problem. This work presents decentralization of swarm robots along-with their methods of optimization, development, applications and implementation in real life domain. It also solves the tr...

Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied toNP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applyin...

I Which size? Trade-off I Minimum size: connectivity by recombination is achieved if at least one instance of every allele is guaranteed to be be present at each locus. Ex: if binary: P∗ 2 = (1− (0.5) ) for l = 50, it is sufficient M = 17 to guarantee P∗ 2 > 99.9%. I Often: independent, uninformed random picking from given search space. I Attempt to cover at best the search space, eg, Latin hyp...