Extremal optimization

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

On some applications of ant colony optimization metaheuristic to structural optimization problems

Abstract: 1.Abstract Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited to solve realworld combinatorial optimization problems. ACO algorithms, published for the first time in 1991 by M. Dorigo and his co-workers, have been applied, particularly starting from 1999 to several kind of optimization problems as the traveling salesman problem, quadratic assignement problem, vehicle routing, sequential ordering, scheduling, graph coloring, management of communications networks and so on. The ant colony optimization metaheuristic takes inspiration from the studies of real ants colonies' foraging behaviour. The main characteristic of such colonies is that individuals have no global knowledge of the problem solving but communicate among them indirectly, depositing on the ground a chemical substance called pheromone, which influences probabilistically the choice of subsequent ants, which tend to follow paths were the pheromone concentration is higher. Such behaviour, called stigmergy, is the basic mechanism which controls ants activity and permits to them to get the shortest path connecting their nest to food source. In this paper it is shown how to convert natural ants behaviour to algorithms able to escape from local minima and find global minimum solution of constrained combinatorial problems. Some examples on plane trusses are also presented. 2.

Extremal optimization for unit commitment problem for power systems

Abstract: This paper introduces extremal optimization (EO) method to solve unit commitment problem for power systems. EO is a local-search heuristic algorithm and originally developed from the fundamentals of statistical physics. In the implementation of EO for unit commitment (UC) problem, a novel problem-specific mutation operator is introduced and rule-based heuristic constraint-repairing techniques are devised. Simulation results on power systems which are composed of up to 100-units over a scheduling horizon of 24-hours demonstrate competitive performance with EO method compared with other existing methods for UC problem.

Design for Two-degree-of-freedom PID Regulator Based on Improved Generalized Extremal Optimization Algorithm

Abstract: A kind of new design method for two-degree-of-freedom(2DOF)PID regulator was presented,in which,a new global search heuristic--improved generalized extremal optimization(GEO)algorithm is applied to the parameter optimization design of 2DOF PID regulator.The simulated results show that very good dynamic response performance of both command tracking and disturbance rejection characteristics can be achieved simultaneously.At the same time,the comparisons of simulation results with the improved GA,the basic GEO and the improved GEO were given.From the comparisons,it is shown that the improved GEO algorithm is competitive in performance with the GA and basic GEO and is an attractive tool to be used in the design of two-degree-of-freedom PID regulator.

Evolutionary Algorithms in Combinatorial Optimization.

Abstract: A package component comprises a main end wall and sidewall segments joining with the main end wall. A flap integrally joining with each sidewall segment overlaps with and is secured to an immediately sidewall segment. The package component may be fabricated as either an inner half or an outer half of two telescopic components and the amount of flap overlap determines whether the component is an inner or outer one. The main end wall areas of the inner and outer components are identical.