Extremal optimization

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

Improved ant agents system by the dynamic parameter decision

Abstract: The ant colony system (ACS) algorithm is a new meta-heuristic for hard combinational optimization problems. It is a population-based approach that uses the exploitation of positive feedback as well as a greedy search. It was first proposed for tackling the well-known traveling salesman problem (TSP). In this paper, we introduce a new version of the ACS based on a dynamic weighted updating method and a dynamic ant number decision method using a curve-fitting algorithm. An implementation to solve the TSP and performance results under various conditions are presented, and a comparison between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with the original ACS in terms of solution quality and computation speed for these problems.

Application of a GEO + SA hybrid optimization algorithm to the solution of an inverse radiative transfer problem

Abstract: In a former study (F.L. de Sousa, F.M. Ramos, F.J.C.P. Soeiro, and A.J. Silva Neto, Application of the generalized extremal optimization algorithm to an inverse radiative transfer problem, Inverse Probl. Sci. Eng. 15 (2007), pp. 699–714), a new evolutionary optimization metaheuristic–the generalized extremal optimization (GEO) algorithm (F.L. de Sousa, F.M. Ramos, P.Paglione, and R.M. Girardi, A new stochastic algorithm for design optimization, AIAA J. 41 (2003), pp. 1808–1818)–was applied to the solution of an inverse problem of radiative properties estimation. A comparison with two other stochastic methods; simulated annealing (SA) and genetic algorithms (GA), was also performed, demonstrating GEO's competitiveness for that problem. In the present article, a recently developed hybrid version of GEO and SA (R.L. Galski, Development of improved, hybrid, parallel, and multiobjective versions of the generalized extremal optimization method and its application to the design of spatial systems, D.Sc. Thesis, ...

Extremal Optimization for protein folding simulations on the lattice

Abstract: This paper presents a novel guided search strategy Extremal Optimization (EO) with constrained structure for protein folding. In the proposed algorithm, evaluating the fitness of each monomer in an amino-acid sequence is introduced to guide the improvement of the conformation. In addition, a constrained structure is proposed to reduce the complexity of algorithm. We demonstrate that EO can be applied successfully to the protein folding problem. The results show that the algorithm can find the best solutions so far for the listed benchmarks. Within the achieved results, the search converged rapidly and efficiently.

An Ant Colony Optimization algorithm for solving the fixed destination multi-depot multiple traveling salesman problem with non-random parameters

Abstract: The Multiple Traveling Salesman Problem (MTSP) is the extension of the Traveling Salesman Problem (TSP) in which the shortest routes of m salesmen all of which start and finish in a single city (depot) will be determined. If there is more than one depot and salesmen start from and return to the same depot, then the problem is called Fixed Destination Multi-depot Multiple Traveling Salesman Problem (MMTSP). In this paper, MMTSP will be solved using the Ant Colony Optimization (ACO) algorithm. ACO is a metaheuristic optimization algorithm which is derived from the behavior of ants in finding the shortest route(s) from the anthill to a form of nourishment. In solving the MMTSP, the algorithm is observed with respect to different chosen cities as depots and non-randomly three parameters of MMTSP: m, K, L, those represents the number of salesmen, the fewest cities that must be visited by a salesman, and the most number of cities that can be visited by a salesman, respectively. The implementation is observed with four dataset from TSPLIB. The results show that the different chosen cities as depots and the three parameters of MMTSP, in which m is the most important parameter, affect the solution.

Optimizing through Co-evolutionary Avalanches

Abstract: We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems The method, called extremal optimization, is inspired by "self-organized criticality," a concept introduced to describe emergent complexity in many physical systems In contrast to Genetic Algorithms which operate on an entire "genepool" of possible solutions, extremal optimization successively replaces extremely undesirable elements of a sub-optimal solution with new, random ones Large fluctuations, called "avalanches," ensue that efficiently explore many local optima Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing With only one adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions Those phase transitions are found in the parameter space of most optimization problems, and have recently been conjectured to be the origin of some of the hardest instances in computational complexity We will demonstrate how extremal optimization can be implemented for a variety of combinatorial optimization problems We believe that extremal optimization will be a useful tool in the investigation of phase transitions in combinatorial optimization problems, hence valuable in elucidating the origin of computational complexity