A DISTRIBUTED APPROACH TO ANT COLONY OPTIMIZATION

Swarm Intelligence(SI) is the emergent collective intelligence of groups of simple agents. Economy is an example of SI. Simulating an economy using Ant Colony algorithms would allow prediction and control of fluctuations in the complex emergent behavior of the simulated system. Such a simulation is far beyond SI's capabilities, which is still in its infancy. This paper presents a distributed approach implementing Ant Colony Optimization(ACO). We present our agent based architecture of ACO and initial experimental results on the Travelling Salesman Problem. The innovation of our work consists of: i)representing network nodes as software agents, ii) representing software agents as software objects that are passed as messages between the nodes according to ACO rules.Swarm Intelligence, Ant Colony Optimization, Multi-Agent, Distributed, Heuristis

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Interacted Multiple Ant Colonies for Search Stagnation Problem

Ant Colony Optimization (ACO) is a successful application of swarm intelligence. ACO algorithms generate a good solution at the early stages of the algorithm execution but unfortunately let all ants speedily converge to an unimproved solution. This thesis addresses the issues associated with search stagnation problem that ACO algorithms suffer from. In particular, it proposes the use of multiple interacted ant colonies as a new algorithmic framework. The proposed framework is incorporated with necessary mechanisms that coordinate the work of these colonies to avoid stagnation situations and therefore achieve a better performance compared to one colony ant algorithm. The proposed algorithmic framework has been experimentally tested on two different NP-hard combinatorial optimization problems, namely the travelling salesman problem and the single machine total weighted tardiness problem. The experimental results show the superiority of the proposed approach than existing one colony ant algorithms like the ant colony system and max-min ant system. An analysis study of the stagnation behaviour shows that the proposed algorithmic framework suffers less from stagnation than other ACO algorithmic frameworks

Pencarian Rute Dinamis Menggunakan Pendekatan Ant Colony Optimization

ABSTRAKSI: Dalam kehidupan selalu melakukan perjalanan dari suatu lokasi ke lokasi lainnya. Dengan meningkatnya jumlah kendaraan bermotor di kota Bandung, maka lalu lintas kota Bandung semakin padat. Jalur terdekat yang terdapat dipusat kota, tidak berarti memberikan waktu tempuh yang cepat tetapi sebaliknya waktu tempuh yang lebih lama dikarenakan kemacetan lalu lintas. Pencarian rute dinamis merupakan permasalahan yang timbul dalam melakukan perjalanan, dimana terjadi perubahan posisi hingga ke lokasi tujuan. Dalam perjalanan pun mungkin terjadi pengalihan rute dari rencana perjalanan yang dilakukan. Dalam tugas akhir ini dibuat sistem pencarian rute dinamis menggunakan pendekatan Ant Colony Optimization. Pemilihan Ant Colony Optimization dikarenakan historis algoritma yang sering dipergunakan dalam permasalahan pencarian rute. Serta kemampuan Ant Colony Optimization dalam menangani perubahan karakteristik sistem walau dalam proses pencarian solusi berlangsung.Kata Kunci : ecerdasan buatan, pencarian rute, rute dinamis, ant colony optimization, ant colony system.ABSTRACT: In life always traveling from one location to another. With the increasing number of vehicles in the city of Bandung, the city traffic more congested. Lane nearest the center of the town there, does not mean giving a fast travel time but otherwise travel time is longer due to traffic congestion. Dynamic routing assignment is an important problem to travel, where there is a change to the position of the destination location. In the course of any possible diversion routes of travel plans are done. In this final project created a dynamic routing assignment system using Ant Colony Optimization approach. Selection of Ant Colony Optimization algorithm because historically that is often used in routing problems. Ant Colony Optimization and the ability to handle changes in system characteristics despite the ongoing process of finding solutions.Keyword: artificial intelligence, routing, dynamic routing, ant colony optimization, ant colony system