Ryosuke Kubota

Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem

We propose an effective hierarchical optimize method HmcDGA.The HmcDGA not only optimizes individuals, but it also optimizes solution space.The HmcDGA can find an optimal solution at low computational cost.The HmcDGA does not require special operations for specific problems.We apply the proposed HmcDGA to the FJSP, and evaluate its effectiveness. In this paper, we propose a new optimization technique, the hierarchical multi-space competitive distributed genetic algorithm (HmcDGA), which is effective for the hierarchical optimization problem. It is an extension of the multi-space competitive distributed genetic algorithm (mcDGA), which was proposed by the authors. The mcDGA efficiently finds an optimal solution with a low computational cost by increasing the number of individuals in a solution space in which it is likely to exist. An optimization method that is divided into several levels of hierarchy is called a hierarchical optimization. Several hierarchical optimization techniques have been proposed, including the hierarchical genetic algorithm (HGA). In hierarchical optimization, a complex problem is divided into a hierarchical collection of simpler problems, and each level is optimized independently. In this way, complex problems can be solved without the need to develop problem-specific operators. However, in the conventional HGA, this results in a high computational cost because the genetic algorithm (GA) is repeated many times at upper and lower level. The HmcDGA is a hybrid of the mcDGA and HGA, and it has some of the advantages of each one; for example, the HmcDGA can find an optimal solution at low computational cost and without requiring special operations. This allows it to be applied to a wide variety of optimization problems. Therefore, the HmcDGA may become the powerful optimization algorithm that can solve various problems. In this paper, we apply the proposed HmcDGA to the flexible job-shop scheduling problem (FJSP) which is one of the complex combinational optimization problem and confirm its effectiveness. Simulation results show that the HmcDGA can find solutions that are comparable to those found by using GAs developed specifically for the FJSP, the HmcDGA is not required a lot of computational costs comparing to the HGA.

Hierarchical optimization using hierarchical multi-competitive genetic algorithm and its application to multiple Vehicle Routing Problem

In this paper a new optimization technique which is effective for hierarchical optimization problem is proposed. This technique is an extension of the multiple-competitive distributed genetic algorithm (mcDGA). This method consists of two levels upper and lower. The solution space to be searched is determined at the upper level, and the optimum solution in a given solution space is determined at the lower level. The migration of the individual and competition is performed at the lower layer thereby optimal solution can be found efficiently. We apply the proposed hierarchical mcDGA to the mVRP to confirm the effectiveness. Simulation result shows the effectiveness of the proposed method.

Multi-space Competitive DGA and its application to localization of multiple signal sources

In this paper, a new optimization method, which is effective for the problem that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of distributed genetic algorithm (DGA), in which each sub-population searches a solution in different space. Based on the competition between sub-populations, population sizes are adequately changed. The proposed method is applied to signal source localization, in which the number of sources is unknown, and simulation results show the effectiveness of the method.

Binary Self-Organizing Map with Modified Updating Rule and Its Application to Reproduction of Genetic Algorithm

In this paper, we propose a modified reproduction strategy of a Genetic Algorithm (GA) utilizing a Self-Organizing Map (SOM) with a novel updating rule of binary weight vectors based on a significance of elements of inputs. In this rule, an updating order of elements is decided by considering fitness values of individuals in a population. The SOM with the proposed updating rule can realize an effective reproduction.

Effective hierarchical optimization using integration of solution spaces and its application to multiple Vehicle Routing Problem

Hierarchical optimization is an optimization method that is divided the problem into several levels of hierarchy. In hierarchical optimization, a complex problem is divided into simpler sub-problems, and each level is optimized independently. Several hierarchical optimization techniques have been proposed, including the hierarchical genetic algorithm (HGA). HGA is organized by multiple genetic algorithms, thereby the computational cost becomes huge depending on the problem. Moreover, the same solution space is searched many times at the upper level, and an unnecessary computational cost takes. In this paper, we propose a new effective searching technique using integration of solution space for hierarchical optimization. The proposed method is based on the Hierarchical multi-space competitive Distributed Genetic Algorithm (HmcDGA), which was proposed by the authors, and the duplicate solution space is integrated at the upper level. The probability of finding optimal solution is improved at lower level, because the population size becomes large by the integration of the solution space. We apply the improved HmcDGA to the multiple Vehicle Routing Problem (mVRP) and show the effectiveness of the proposed method.

Signal sources localization using GA preserving genetic diversity based on fitness values

The signal sources localization is a very important study, and many researchers work on this problem by various methods. A genetic algorithm (GA) is probabilistic search method based on a process of the evolution. GA is applied to many real problems, because it has high versatility and search ability. However, in the conventional GA, the reproduction operator causes a lack of genetic diversity, and search efficiency decreases. To solve this problem, the GA in consideration of genetic diversity was proposed. This method can search the optimal solution faster than a conventional GA. However, the parameter which define a degree of variability have to be decided depending on a given problem. In this paper, we propose the new method in which the degree of variability can be adaptively defined based on the fitness values and the spatial distribution of the individuals. The effectiveness of the proposed method is verified by applying it to the signal sources localization using a simple head model.