Ant colony optimization

Abstract-In this paper an efficient hybrid optimization approach is used to solve the optimal power flow problem. In the proposed approach particle swarm optimization along with ant colony optimization is used for setting of control variables for optimal power flow problem. The proposed approach is tested and examined in standard IEEE 30 bus test system. The various objective functions involved are fuel cost minimization, voltage profile improvement and transmission loss reduction. This proposed

Abstract Ant colony optimization is a technique for solving problem which are hard in nature. These kinds of problems need only the optimal solution. ACO provide a solution based on the behaviour of ants searching for food. This paper list out various techniques used to solve such problems and their advantages, disadvantages. Through this review we identify some suggestion for solving NP hard problem. Keywords :Ant colony optimization, NP hard, stigmergy, Pheromone, optimal , heuristics 1. Introduction

1.6 Ant Colony Optimization: 1.6.1 Introduction The conception of massive Gordian behavior emerging from the behavior of many relatively simple units, and the dealings with them, is fundamental to the field of Artificial Intelligence. The growing understanding of such systems offers the prospect of creating artificial systems which are controlled by such significant shared behavior; in particular, we believe that the sweating of this concept might lead to entirely new channels for the pilotage

GAVNS: Guided Ant Colony Optimization based Variable Neighborhood Search for Optimistic Load Balancing in Grid Computing Gurveer Kaur Brar1, Amit Chhabra2 1 Guru Nanak Dev University, Amritsar Punjab, India gurveer.dhillon43@gmail.com, amit.cse@gndu.ac.in Abstract Grid Computing resolves high performance computing and throughput issues through sharing of resources. These resources are heterogeneous in nature and geographically distributed to develop large scale applications. Scheduling

Nature-Inspired Metaheuristic Optimization Algorithms-A Review Pragati Loomba Sonali Tiwari And Neerja Negi Student, Faculty of Computer Applications Assistant Professor, Faculty of Computer Applications Manav Rachna International University Manav Rachna International University Faridabad Faridabad loombapragati.pl@gmail.com sonali.fca@mriu.edu.in neerja.fca@mriu.edu.in Abstract - Now a day nature-inspired algorithms become a current trend and is applicable to almost every area. This paper

Now-a-days optimization and testing in software engineering using traditional techniques has become a tedious task. In order to fasten this process, search based-software engineering (SBSE) techniques are introduced to solve real world large scale problems efficiently. SBSE techniques consisting of several algorithms can be implemented throughout the software development life cycle in order to deliver a more reliable product. This paper reviews the application of five major SBSE methods and the issues

......................................................... 2 Swarm Intelligence (SI) Models ......................................................................................... 4 2.1 Ant Colony Optimization (ACO) Model ........................................................................ 4 2.1.1 Ants in

methods are useful in problems that need to find paths to goals. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Real ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate better solutions. One variation on this approach is the bees algorithm

space such as in ant colony optimization and evolutionary computation techniques. The mode of origin is another basis to distinguish between nature inspired and no nature inspired metaheuristic algorithm. Evolutionary computation and Ant Colony Optimization belongs to the class of nature inspired whereas tabu search and iterated local search belongs to the class of non nature inspired algorithms. Metaheuristic algorithms are also known as search based techniques or optimization algorithms which when

Mosquito Flying Optimization (MFO) Subtitle as needed (paper subtitle) Md. Alauddin, Assistant Professor, Deptt. of Petroleum Studies, AMU, Aligarh, India Abstract— This is a new optimization algorithm which mimic the behavior of mosquito to find a hole in mosquito net, if any. Both the flying and sliding motion of the mosquito have been modelled and incorporated in the algorithm. The algorithm was tested for global minima on different type of benchmark functions of various dimension and