Péter Földesi

Modeling of loss aversion in solving fuzzy road transport traveling salesman problem using eugenic bacterial memetic algorithm

The aim of the traveling salesman problem (TSP) is to find the cheapest way of visiting all elements in a given set of cities and returning to the starting point. In solutions presented in the literature costs of travel between nodes (cities) are based on Euclidean distances, the problem is symmetric and the costs are constant and crisp values. Practical application in road transportation and supply chains are often fuzzy. The risk attitude depends on the features of the given operation. The model presented in this paper handles the fuzzy, time dependent nature of the TSP and also gives solution for the asymmetric loss aversion by embedding the risk attitude into the fitness function of the bacterial memetic algorithm. Computational results are presented as well.

A novel pose estimation algorithm for robotic navigation

This paper proposes a new pose estimation algorithm in the framework of robotic navigation problems. The algorithm gives the mobile robot (MR) pose on the basis of the difference between the MR desired pose and the MR current pose. In this regard the MR sensor readings and the readings of a virtual sensor are employed. The algorithm is advantageous in comparison with other pose estimation algorithms including those based on classical filter approaches because of the small computation time. Simulation and real-world experimental results are included to illustrate the effectiveness of the pose estimation algorithm and its potential for integration in MR control structures and algorithms. A new pose estimation algorithm is proposed.The difference between the mobile robot desired and current pose is computed.The mobile robot sensor readings and the virtual sensor readings are employed.The algorithm is tested by simulation and real-world experimental results.

Novel calculation of fuzzy exponent in the sigmoid functions for fuzzy neural networks

This paper presents a novel calculation of fuzzy exponent in the sigmoid functions for fuzzy neural networks. The investigated fuzzy neural network applies fuzzy input signals and crisp connection weights in the networks hidden and output layers. The applied calculation of fuzzy exponent is based on a parametric representation of the fuzzy exponent that is able to provide a crisp output instead of the extension principles fuzzy output and requires significantly less computational effort than the learning based on @a-cuts. For the training of the network the bacterial memetic algorithm is applied which effectively combines the bacterial evolutionary algorithm with gradient based learning. The method is tested on a benchmark problem and on two real datasets. Comparison to the classical technique concerning the learning time is also provided in the paper.

An effective Discrete Bacterial Memetic Evolutionary Algorithm for the Traveling Salesman Problem

In recent years, a large number of evolutionary and other population‐based heuristics were proposed in the literature. In 2009, we suggested to combine the very efficient bacterial evolutionary algorithm with local search as a new Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) (Farkas et al., In: Towards intelligent engineering & information technology, Studies in Computational Intelligence, Vol 243. Berlin, Germany: Springer‐Verlag; 2009. pp 607–625). The method was tested on one of Traveling Salesman Problem (TSP) benchmark problems, and a difference was found between the real optimum calculated by the new and the published result because the Concorde and the Lin–Kernighan algorithm use an approximation substituting distances of points by the closest integer values. We modified the Concorde algorithm using real cost values to compare with our results. In this paper, we systematically investigate TSPLIB benchmark problems and other VLSI benchmark problems (http://www.math.uwaterloo.ca/tsp/vlsi/index.html) and compare the following values: optima found by the DBMEA heuristic and by the modified Concorde algorithm with real cost values, run times of DBMEA, modified Concorde, and Lin–Kernighan heuristic. In this paper, for the evaluation of metaheuristic techniques, we suggest the usage of predictability of the successful run in addition to the accuracy of the result and the computational cost as third property. We will show that in the case of DBMEA, the run time is more predictable than in the case of Concorde algorithm, so we suggest the use of DBMEA heuristic as very efficient for the solution of TSP and other nondeterministic polynomial‐time hard optimization problems.

Cooperation in multiagent systems

This paper gives aspects related to a cooperation scenario in the framework of multiagent systems. The presentation is focused on a multiagent system that consists of two agents, the Master and the Apprentice. The theoretical basis of the cooperation scenario is the definition of the most probable process, and two algorithms are used with this regard. The formulation of the cooperation scenario is exemplified for a case study that builds an architecture of successively placed bricks in the workspace.

A discrete bacterial memetic evolutionary algorithm for the traveling salesman problem

This paper presents a Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) for the Traveling Salesman Problem. This algorithm combines the very efficient bacterial evolutionary algorithm with 2-opt and 3-opt local searches. Our approach was tested on TSPLIB and other VLSI benchmark problems. In this paper our computational results (minimal tour lengths, run times) are compared with other efficient TSP solver algorithms (Lin-Kernighan, Concorde). We will show that in significant number of the published benchmark problems the optimal tour was not found by the Concorde algorithm and the Lin-Kernighan heuristic because this approaches use an approximation substituting distances of points by the closest integer values. We suggest the substitution of the benchmark result set by the real optima calculated by the new DBMEA algorithm and the use of DBMEA heuristic as more precise for the solution of TSP and other NP-hard optimization problems.

Enhanced discrete bacterial memetic evolutionary algorithm - An efficacious metaheuristic for the traveling salesman optimization

Abstract In this paper we present a novel universal metaheuristic, Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA), which is based on the combination of the Bacterial Evolutionary Algorithm and local search techniques, used for solving NP-hard optimization problems. The algorithm was tested on a series of symmetric Traveling Salesman Problems (TSP) and Traveling Salesman Problem with time windows (TSPTW) benchmarks. The size of the symmetric TSP benchmarks went up to 5 000 cities. In all cases the DBMEA algorithm produced optimal or near-optimal solutions and the difference from the known best values was within 0.16%. While for large size problems it was much faster than the Concorde solver, it was found to be slower compared to the Helsgaun-Lin-Kernighan heuristic, which is the most efficient TSP solver method. With some slight modifications the same algorithm was also tested on TSP with time windows (TSPTW) benchmark instances. In most cases the DBMEA procedure found the known best solutions, and it was again the second fastest method compared with the state-of-the-art techniques for the TSPTW. DBMEA is called efficacious because it is a universal method. It can be efficiently applied to various NP-hard optimization problems and, as in all cases, it results in the optimal or a very near-optimal solutions, while its runtime is very predictable in terms of the size of the problem, and the topology of the instance does not affect its runtime significantly. Even though heuristics developed for a particular type of problem might perform better for that restricted class, our novel method proposed here is universally applicable and may be deployed successfully for optimizing other discreet NP-hard graph search and optimization problems as well.

A New State Reduction Approach for Fuzzy Cognitive Map with Case Studies for Waste Management Systems

The authors have investigated the sustainability of Integrated Waste Management Systems (IWMS). These systems were modeled by Fuzzy Cognitive Maps (FCM), which are known as adequate fuzzy-neural network type models for multi-component systems with a stable state. The FCM model was designed of thirty-three factors to describe the real world processes of IWMS in as much detailed and as much accurately as possible. Although, this detailed model meets the requirements of accuracy, the presentation and explanation of such a complex model is difficult due to its size.

Strategic Decision Support in Waste Management Systems by State Reduction in FCM Models

In this paper, we introduce a new design for modeling sustainable waste management systems. By its complexity, this model is much more precise in describing the real systems than those found in the relevant literature. We set up a model with six factors and then decomposed the constituting factors up to around thirty subcomponents, thereby established an extremely complex and completely novel model of the Integrated Waste Management System (IWMS) using the system-of-system (SoS) approach with the help of experts. After the investigation of the basic and detailed model and their connection matrices, the following idea arises. The two models differ conceptually and so greatly that less than thirty-three factors should be enough to approximately describe the mechanism of action of the real IWMS. In the following, a new state reduction method is proposed. It can be considered as a generalization of the state reduction procedure of sequential systems and finite state machines. The essence of the proposal is to create clusters of factors and to build a new model using these clusters as factors. This way the number of factors can be decreased to make the model easier to understand and use. Our main goal with this method is to support the strategic decision making process of the stakeholder in order to ensure the long-term sustainability of IWMS.

Interpretation of Loss Aversion in Kano’s Quality Model

For designing and developing products/services it is vital to know the relevancy of the performance generated by each technical attribute and how they can increase customer satisfaction. Improving the parameters of technical attributes requires financial resources, and the budgets are generally limited. Thus the optimum target can be the achievement of the minimum overall cost for a given satisfaction level. Kano’s quality model classifies the relationships between customer satisfaction and attribute-level performance and indicates that some of the attributes have a non-linear relationship to satisfaction, rather power-function should be used. For the customers’ subjective evaluation these relationships are not deterministic and are uncertain. Also the cost function are uncertain, where the loss aversion of decision makers should be considered as well. This paper proposes a method for fuzzy extension of Kano’s model and presents numerical examples.