Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...

Ant colony optimization (ACO) is a technique for optimization that was introduced in the early 1990’s. The inspiring source o f ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete and continuous optimization problems and to important problems in telecommunications, such a...

Research in software testing has experienced a significant growth in recent years. One topic of special interest is fault detection techniques to reduce human interference and detect maximum faults. Priority is given to these techniques so that only higher priority techniques should be used instead of using individual techniques of lower priority. Bee Colony Optimization based upon natural phen...

Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligen...

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...

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages long running time and easy stagnation still restrict its further wide application many fields. In this study, saltatory evolution (SEACO) is proposed to incr...

The Learnable Ant Colony Optimization (LACO) is proposed to satellite ground station system scheduling problems. The LACO employs an integrated modelling idea which combines the ant colony model with the knowledge model. In order to improve the performance, LACO largely pursues the complementary advantages of ant colony model and knowledge model. Experimental results suggest that LACO is a feas...

Hybrid algorithm is proposed to solve combinatorial optimization problem by using Ant Colony and Genetic programming algorithms. Evolutionary process of Ant Colony Optimization algorithm adapts genetic operations to enhance ant movement towards solution state. The algorithm converges to the optimal final solution, by accumulating the most effective sub-solutions.

This paper presents an ant colony optimization based algorithm to solve real parameter optimization problems. In the proposed method, an operation similar to the crossover of the genetic algorithm is introduced into the ant colony optimization. The crossover operation with Laplace distribution following a few promising descent directions (FPDD-LX) is proposed to be performed on the pheromone of...

In this paper we try to introduce the Ant Colony Optimization (ACO) and its application in Economic Dispatch ED). In the step one we present theory and performance of ant colony and its history and its application in various applications and how work the ant colony algorithm. In step two we present ant colony optimization include Basic Concept, Ant Searching Behavior, Path Retracing and Pheromo...