Extremal optimization

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

Cost coefficient control method for solving optimization problems on Hopfield-type neural networks

Abstract: When solving optimization problems on a Hopfield-type neural network, a constraint coefficient and cost coefficient of an energy function should be determined appropriately. Until recently, the values of these coefficients were decided based on experience and trial and error. Therefore, solutions that satisfy the constraints could not be obtained and the quality of the solutions was not good. In order to avoid this problem, we propose a method to control cost coefficient values automatically while keeping a constraint coefficient to be constant. We applied this method to the Travelling Salesman Problem, and obtained near-optimal solutions more efficiently than other approaches. The proposed algorithm is very effective especially for the difficult city allocations.

Population-based Modified Extremal Optimization for Contact Map Overlap Maximization Problem

Abstract: The three-dimensional structures of proteins provide biogenic functions for biological activities. Proteins that have similar three-dimensional structures usually have similar biological functions. Therefore, many researchers focus on the techniques for comparing the three-dimensional structures of proteins. Many of these techniques for comparing protein structures are based on protein structure alignment, which is one of the most effective methods for extracting similar strutures. The Contact Map Overlap (CMO) maximization problem (for short, the CMO problem) is formulated as a combinatorial optimization for finding the optimal structure alignments. In this paper, we propose a novel bio-inspired heuristic using Population-based Modified Extremal Optimization (PMEO) for the CMO problem. The proposed heuristic has two features. First, the proposed heuristic uses PMEO. There are multiple individuals in a population which repeat alternation of generations. Second, to improve the search efficiency, individuals copy a sub-structure of an individual with good sub-structures at each alternation of generations.

Attraction force optimization (AFO): a deterministic nature-inspired heuristic for solving optimization problems in stochastic simulation

Abstract: The paper presents a new optimization heuristic called AFO Attraction Force Optimization, able to maximize discontinuous, non-differentiable and highly nonlinear functions in discrete simulation problems. The algorithm was developed specifically to overcome the limitations of traditional search algorithms in optimization problems performed on discreteevent simulation models used, for example, to study industrial systems and processes. Such applications are characterized by three particular aspects: the response surfaces of the objective function is not known to the experimenter, a few number of independent variables are involved, very high computational time for each single simulation experiment. In this context it is therefore essential to use an optimization algorithm that on one hand tries to explore as effectively as possible the entire domain of investigation but, in the same time, does not require an excessive number of experiments. The article, after a quick overview of the most known optimization techniques, explains the properties of AFO, its strengths and limitations compared to other search algorithms. The operating principle of the heuristic, inspired by the laws of attraction occurring in nature, is discussed in detail in the case of 1, 2 and Ndimensional functions from a theoretical and applicative point of view. The algorithm was then validated using the most common 2-dimensional and N-dimensio990 I. Bendato et al. nal benchmark functions. The results are absolutely positive if compared, for the same initial conditions, with the traditional methods up to 10-dimensional vector spaces. A higher number of independent variables is generally not of interest for discrete simulation optimization problems in industrial applications (our research field).

DEVOpT: a distributed architecture supporting heuristic and metaheuristic optimization methods

Abstract: This paper presents a distributed software architecture that allows the cooperation among research institutions in the field of Combinatorial Optimization --- DEVOpT: Distributed Evolutionary Optimization Centers. It has as main aims to share existing algorithms for optimization problems, to allow the easy testing of these algorithms with existing instances, to provide fast and better ways to design new algorithms, and to share computational power among the cooperating institutions. This is achieved respecting the autonomy and heterogeneity of the cooperating institutions. The distributed architecture is discussed here and also a case study of a Parallel Memetic Algorithm to solve the Asymmetric Traveling Salesman Problem (ATSP) running on this environment is analyzed.