Extremal optimization

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

Speeding-Up ACO Implementation by Decreasing the Number of Heuristic Function Evaluations in Feature Selection Problem

Abstract: In this paper we study a model to feature selection based on Ant Colony Optimization and Rough Set Theory. The algorithm looks for reducts by using ACO as search method and RST offers the heuristic function to measure the quality of one feature subset. Major results of using this approach are shown and others are referenced. Recently, runtime analyses of Ant Colony Optimization algorithms have been studied also. However the efforts are limited to specific classes of problems or simplified algorithm’s versions, in particular studying a specific part of the algorithms like the pheromone influence. From another point of view, this paper presents results of applying an improved ACO implementation which focuses on decreasing the number of heuristic function evaluations needed.

General method for evaluating optimization algorithm and its application

Abstract: There are many kinds of evaluation methods for heuristic algorithms,but their evaluation standards are different.Through analyzing the various factors affecting algorithm performance,this paper presented a general method to evaluate heuristic optimization algorithms based on their commonnesses.Learning Algorithm(LA) was introduced to find the most concise version of the optimization algorithm.The general evaluation method was also applied in the evaluation of LA.A conclusion is drawn that LA is a kind of effective heuristic optimization algorithm,and its optimized performance is superior to the classical algorithms,such as Genetic Algorithm(GA) and Particle Swarm Optimization(PSO).

Extremal Optimization Approach to Joint Routing and Scheduling for Industrial Wireless Networks

Abstract: A technique based on the Extremal Optimization approach is developed to solve joint routing and link scheduling problems in Industrial Wireless Networks. Numerical results show that this technique provides a suitable solution with fast computation capacity.

Simulations of Energy Fluctuations in the Sherrington-Kirkpatrick Spin Glass

Abstract: The scaling of fluctuations in the distribution of ground-state energies or costs with the system size N for Ising spin glasses is considered using an extensive set of simulations with the Extremal Optimization heuristic across a range of different models on sparse and dense graphs. These models exhibit very diverse behaviors, and an asymptotic extrapolation is often complicated by higher-order corrections. The clearest picture, in fact, emerges from the study of graph-bipartitioning, a combinatorial optimization problem closely related to spin glasses. Aside from two-spin interactions with discrete bonds, we also consider problems with Gaussian bonds and three-spin interactions, which behave differently to a significant degree.

Traveling salesman problem optimization with parallel genetic algorithm

Abstract: The TSP is a NP-hard problem in optimization studied in both operations research and computer science. Metaheuristics are efficient alternative techniques for NP-hard and greater dimensional problems and they are impossible to solve by classic mathematical techniques. The paper describes the use of MATLAB's Parallel Computing Toolbox for parallel genetic algorithm based design of a TSP. Parallel genetic algorithms (PGA) represents a stochastic optimization approach which is computed in co-operating and interconnected computation nodes in a parallel mode. Each node of the PGA can be located on the same processor, or on more processors or on more computers respectively. We also carried on the computation of a TSP example which shows a higher speedup and a better per-formance.