Extremal optimization

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

Solving multiprocessor scheduling problem with GEO metaheuristic

Abstract: We propose a solution of the multiprocessor scheduling problem based on applying a relatively new metaheuristic called Generalized Extremal Optimization (GEO). GEO is inspired by a simple coevolutionary model known as Bak-Sneppen model. The model assumes existing of an ecosystem consisting of N species. Evolution in this model is driven by a process in which the weakest species in the ecosystem, together with its nearest neighbors is always forced to mutate. This process shows characteristic of a phenomenon called a punctuated equilibrium which is observed in evolutionary biology. We interpret the multiprocessor scheduling problem in terms of the Bak-Sneppen model and apply the GEO algorithm to solve the problem. We show that the proposed optimization technique is simple and yet outperforms both genetic algorithm (GA)-based and particle swarm optimization (PSO) algorithm-based approaches to the multiprocessor scheduling problem.

A multi-swarm cooperative hybrid particle swarm optimizer

Abstract: Cooperative approaches have proved to be very useful in evolutionary computation. This paper a novel multi-swarm cooperative particle swarm optimization (PSO) is proposed. It involves a collection of two sub-swarms that interact by exchanging information to solve a problem. The two swarms execute IPSO (improved PSO) independently to maintain the diversity of populations, while introducing extremal optimization (EO) to IPSO after running fixed generations to enhance the exploitation. States of the particles are updated based on global best particle that has been searched by all the particle swarms. Synchronous learning strategy and random mutation scheme are both absorbed in our approach. Simulations on a suite of benchmark functions demonstrate that this method can improve the performance of the original PSO significantly.

Solving Travelling Salesmen Problem using Ant Colony Optimization Algorithm

Abstract: Ant Colony Optimization is a new meta-heuristic technique used for solving different combinatorial optimization problems. ACO is based on the behaviors of ant colony and this method has strong robustness as well as good distributed calculative mechanism. ACO has very good search capability for optimization problems. Travelling salesman problem is one of the most famous combinatorial optimization problems. In this paper we applied the ant colony optimization technique for symmetric travelling salesperson problem. Analysis are shown that the ant select the rich pheromone distribution edge for finding out the best path.

An Improved Ant Colony Algorithm Based on 3-Opt and Chaos for Travelling Salesman Problem

Abstract: This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial optimization problems. The existing algorithms still have some imperfections, we use a combination of two different operators to improve the performance of algorithm in this work. First, 3-opt local search is used as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions. Therefore, it’s better and more effective algorithm for TSP.