Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Ant Colony System with Heuristic Function for the Travelling Salesman Problem

Abstract: Ant colony system which is classified as a meta-heuristic algorithm is considered as one of the best optimization algorithm for solving different type of NP-Hard problem including the travelling salesman problem. A heuristic function in the Ant colony system uses pheromone and distance values to produce heuristic values in solving the travelling salesman problem. However, the heuristic values are not updated in the entire process to reflect the knowledge discovered by ants while moving from city to city. This paper presents the work on enhancing the heuristic function in ant colony system in order to reflect the new information discovered by the ants. Experimental results showed that enhanced algorithm provides better results than classical ant colony system in term of best, average and standard of the best tour length. Keywords : Ant Colony Optimization, Ant Colony System, Heuristic Function, Traveling Salesman Problem 1. Introduction Biological ants have the ability to discover the shortest route from the nest to the source of food [1]. Although they do not have an advanced vision system [2], they have the ability to communicate with the environment. Ants use a chemical substance called a “pheromone” to communicate with the environment and between each other [3]. Pheromone substance has an evaporation property which is a powerful mechanism to update the route information. While an ant moves looking for food, it deposits a pheromone along the path. The following ant will, more likely, select the route with richer pheromones. This mechanism will make the ant choose the shortest path. In 1992, Marco Dorigo proposed the first Ant Colony Optimization (ACO) algorithm to search for an optimal solution in graphs to solve optimization problems such as the travelling salesman problem, job scheduling and network routing [1]. The variants of ACO are: (i) Ant System (AS) [4] [5] [6]. (ii) The first improvement on the ant system, called the Elitist strategy for Ant System (EAS) [7]. The improvement was done by providing strong additional reinforcement to the arcs belonging to the best tour found since the start of the algorithm. (iii) Rank-Based Ant System (AS

Workflow Scheduling Using Heuristics Based Ant Colony Optimization

Abstract: Ant Colony Optimization (ACO) is a Meta heuristic combinatorial optimization technique. It is a excellent grouping optimization procedure. A novel ant colony optimization is projected. To advance the penetrating routine the principles of evolutionary algorithm and simulated resistant algorithm have been pooled with the distinctive continuous Ant colony optimization algorithm. In this new algorithm, the ant individual is transformed by adaptive cauchi transformation and thickness selection. To verify the new algorithm the typical functions such as objective function and path construction functions are used. And then the results are versified with continuous ant colony optimization algorithm. The results show that the convergent speed and computing precision of new algorithm are all very good. We can use the algorithm to solve the real time problems like routing, assignment, scheduling.

An Approach to Combinatorial Problems by MapReduce Based Ant Colony Optimization

Abstract: Ant Colony Optimization (ACO) is a kind of Meta heuristics approach which is simulated from the social behaviour of ants. It could be a good alternative approach to solve NP hard combinatorial optimization problems such as 0-1 knapsack problem and the Traveling Salesman Problem (TSP).The ACO can get a solution that is quite nearer to the optimal solution; however premature input bogs the system down. Parallelization is an effective way to solve large-scale ant colony optimization problems, since the better solution requires larger number of ants and iterations which consume more time. The problem can be solved by Map Reduce based ACO approach

A Parallel and Distributed Topological Approach to 3D IC Optimal Layout Design

Abstract: The task of 3D ICs layout design involves the assembly of millions of components taking into account many different requirements and constraints such as topological, wiring or manufacturability ones. It is a NP-hard problem that requires new non-deterministic and heuristic algorithms. Considering the time complexity, the commonly applied Fiduccia-Mattheyses partitioning algorithm is superior to any other local search method. Nevertheless, it can often miss to reach a quasi-optimal solution in 3D spaces. The presented approach uses an original 3D layout graph partitioning heuristics implemented with use of the extremal optimization method. The goal is to minimize the total wire-length in the chip. In order to improve the time complexity a parallel and distributed Java implementation is applied. Inside one Java Virtual Machine separate optimization algorithms are executed by independent threads. The work may also be shared among different machines by means of The Java Remote Method Invocation system.

A Heuristic Optimization Algorithm in System Structure Optimization

Abstract: Systems structure optimization is a multi-objective or a combinatorial optimization problem, which should consider the comprehensive influence of cost, time and resource etc. This paper firstly describes the system structure optimization problem and gives the mathematical model. Then a heuristic optimization algorithm is proposed to analyze the execution time, the success rate and the cost in system structure optimization. And the efficiency of the heuristic optimization algorithm in searching for the optimal system structure solution is compared with genetic algorithm and particle swarm optimization method. At last, the experiment simulation verifies the validity and the correctness of the proposed algorithm.