Pedro Lopez-Garcia

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Students t-test, the Holms test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization in GACE is made. These range from a pure GA to a pure CE, passing through different weights for each of the combined techniques. The results prove that GACE is more accurate than GA or CE alone for predicting short-term traffic congestion.

GACE: A meta-heuristic based in the hybridization of Genetic Algorithms and Cross Entropy methods for continuous optimization

Abstract Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybridization of techniques. This paper presents a hybrid metaheuristic that combines a Genetic Algorithm (GA) with a Cross Entropy (CE) method to solve continuous optimization functions. The algorithm divides the population into two sub-populations, in order to apply GA in one sub-population and CE in the other. The proposed method is tested on 24 continuous benchmark functions, with four different dimension configurations. First, a study to find the best parameter configuration is done. The best configuration found is compared with several algorithms in the literature in order to demonstrate the competitiveness of the proposal. The results shows that GACE is the best performing method for instances with high dimensionality. Statistical tests have been applied, to support the conclusions obtained in the experimentation.

A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

Abstract The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bats distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedmans non-parametric test and the Holms post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

On Efficiently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm with Random Reinsertion Operators

An evolutionary and discrete variant of the Bat Algorithm (EDBA) is proposed for solving the Vehicle Routing Problem with Time Windows, or VRPTW. The EDBA developed not only presents an improved movement strategy, but it also combines with diverse heuristic operators to deal with this type of complex problems. One of the main new concepts is to unify the search process and the minimization of the routes and total distance in the same operators. This hybridization is achieved by using selective node extractions and subsequent reinsertions. In addition, the new approach analyzes all the routes that compose a solution with the intention of enhancing the diversification ability of the search process. In this study, several variants of the EDBA are shown and tested in order to measure the quality of both metaheuristic algorithms and their operators. The benchmark experiments have been carried out by using the 56 instances that compose the 100 customers Solomon’s benchmark. Two statistical tests have also been carried out so as to analyze the results and draw proper conclusions.

Hybridizing Genetic Algorithm with Cross Entropy for Solving Continuous Functions

In this paper, a metaheuristic that combines a Genetic Algorithm and a Cross Entropy Algorithm is presented. The aim of this work is to achieve a synergy between the capabilities of the algorithms using different population sizes in order to obtain the closest value to the optimal of the function. The proposal is applied to 12 benchmark functions with different characteristics, using different configurations.

Comparison between Golden Ball Meta-heuristic, Evolutionary Simulated Annealing and Tabu Search for the Traveling Salesman Problem

The Golden Ball is a multi-population meta-heuristic based on soccer concepts. It was first designed to solve combinatorial optimization problems. Until now, it has been tested with different kind of problems, but its efficiency has only been compared with some classical algorithms, such as different kind of Genetic Algorithms and Distributed Genetic Algorithms. In this work, the performance of the Golden Ball is compared with the ones obtained by two famous and widely used techniques: an Evolutionary Simulated Annealing and a Tabu Search. These both meta-heuristics are two of the most used ones along the history for solving optimization problems. In this first study, the comparison is performed for the well-known Traveling Salesman Problem.

Short-Term Traffic Congestion Forecasting Using Hybrid Metaheuristics and Rule-Based Methods: A Comparative Study

In this paper, a comparative study between a hybrid technique that combines a Genetic Algorithm with a Cross Entropy method to optimize Fuzzy Rule-Based Systems, and literature techniques is presented. These techniques are applied to traffic congestion datasets in order to determine their performance in this area. Different types of datasets have been chosen. The used time horizons are 5, 15 and 30 min. Results show that the hybrid technique improves those results obtained by the techniques of the state of the art. In this way, the performed experimentation shows the competitiveness of the proposal in this area of application.

A Parallel Meta-heuristic for Solving a Multiple Asymmetric Traveling Salesman Problem with Simulateneous Pickup and Delivery Modeling Demand Responsive Transport Problems

Transportation is an essential area in the nowadays society. Due to the rapid technological progress, it has gained a great importance, both for business sector and citizenry. Among the different types of transport, one that has gained notoriety recently is the transportation on-demand, because it can affect very positively the people quality of life. There are different kinds of on-demand transportation systems, being the Demand Responsive Transit (DRT) one of the most important one. In this work, a real-life DRT problem is proposed, and modeled as a Rich Traveling Salesman Problem. Specifically, the problem presented is a Multiple Asymmetric Traveling Salesman Problem with Simultaneous Pickup and Delivery. Furthermore, a benchmark for this new problem is also proposed, and its first resolution is offered. For the resolution of this benchmark the recently developed Golden Ball meta-heuristic has been implemented.

Applications of Soft Computing in Intelligent Transportation Systems

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks.